ISSN XXXXXXXXXX International Research Journal of Applied Finance Volume.II Issue.6 June 2011 Contents Testing for Seasonality in Option and Calendar Month: An Empirical Investigation on the US Major...

ISSN 2229-6891
International Research Journal of
Applied Finance
Volume.II Issue.6 June 2011
Contents
Testing for Seasonality in Option and Calendar Month: An Empirical Investigation on the
US Major Index Components
590 - 608
Rafiqul Bhuyan
Determinants Influencing the Seasoned Equity Offerings: Private Placements vs Rights
Issue
609 - 621
Norhanim Dewa & Izani Ibrahim
Effect on Security Prices and Volatility from Cross Listing within the GCC Markets
622 - 630
Dr. Abraham, Abraham
Cash Flow-Investment Sensitivity for Manufacturing Firms in America, Japan and Taiwan
631 - 641
Feng-Li Lin & Jui-Ying, Hung
Determinants of the ‘Decision to Finance’ in Micro Finance Institutions
642 - 682
Prof. Fedhila Hassouna & Dr. Mehdi Mejdoub
Cost of equity in emerging markets. Evidence from Romanian listed companies
683 - 691
Costin Ciora
Corporate Events’ Effect on Stock Returns: Evidence from Athens Stock Exchange
692 - 715
Aristeidis Samitas, Dimitris Kenourgios & Ioannis Tsakalos
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International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
589
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International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
590
Testing for Seasonality in Option and Calendar Month: An Empirical
Investigation on the US Major Index Components
Rafiqul Bhuyan, PhD
Associate Professor of Finance
Dept. of Accounting & Finance
College of Business & Public Administration
California State University
San Bernardino
CA 92407
[email protected]
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
591
Abstract
Using all securities from Dow 30, S&P 100 and S&P 500 indices respectively we show that
Option month based monthly returns and volatilities are different from those of calendar month.
These differences may explain the worthiness of options contracts on call and put options when
they expire. We conclude the option expiration effect may explain the differences in return in
option month compared to the calendar month. Our result contributes to the existing literature by
offering evidence of return differences in option month what support the option expiration effect.
When security returns are analyzed based on calendar month and option month to test for
seasonality, our results support the findings of the existing literature in terms of the calendar
month. Our findings contribute to the literature by adding the result that when security returns
are analyzed based on the option month; it also shows the seasonal pattern. Our both results
could be the added evidence against the weak-form market efficiency.
I. Introduction
Seasonality in financial market is widely investigated in finance literature. It is addressed by
investigating the abnormal returns in the month of January, day of the week, tax loss selling
effect, among other effects. When analyzing the January effect, it is the calendar month January
effect addressed in the literature. However, there remains to be seen the effect of option month in
return pattern. Option month’s beginning and ending dates are from two calendar months. The
third Friday of a month marks the ending day of the option month and the Monday after the third
Friday marks the First day of the option month. Trillions of dollar transaction takes place in
option markets to profit from the movement of the underlying assets. That makes the option
month a special case and event for stock market. The intent of this current research is to analyze
the option month effect whether it adds as an additional anomaly in financial market.
II. Literature Review
The January effect has been widely studied to see if a profitable investment strategy exists. The
key explanations for the January effect are: the year-end tax-loss-selling hypothesis (e.g.,
Branch (1977), Dyl (1977), and Schultz (1985)); the window-dressing hypothesis (e.g.,
Haugen and Lakonishok (1988), and Ritter and Chopra (1989)); turn-of- the-year 'liquidity'
hypothesis (e.g., Ogden (1990)); accounting information hypothesis (e.g., Rozeff and Kinney
(1976)), and bid-ask spread (e.g., Keim (1989)). However, Bhardwaj and Brooks (1992)
conclude that for typical investors, the January anomaly of low-price stocks outperforming
high-price stocks cannot be used to earn abnormal returns. Mills and Coutts (1995) report that
even if calendar effects are persistent in their occurrence and magnitude, the costs of
implementing trading rules is prohibitive. Draper and Paudyal (1997) find that although it
appears to be feasible to earn a high nominal return by trading on seasonality, it does not appear
to be feasible to earn excess returns after allowance for transaction costs. Booth and Keim
(2000) also conclude that the January effect is 'alive' but difficult to capture.
On the other hand, Ko (1998) gives some favorable evidence on the economic
exploitation of seasonalities. Specifically, he investigates the effects of international
diversification on the stock market monthly seasonality from an economic point of view. He
finds that the strategy using monthly seasonality outperforms a buy-and-hold strategy. De Bondt,
Thaler and Bernstein (1985), found that investors over-reacted to unexpected news. Stocks that
performed well in the previous periods (winners), and stocks that performed poorly in the
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
592
previous periods (losers), both tended to revert back to their mean value in the subsequent
periods. In a psychological study, Kahneman and Tversky (1982) document individuals overreacting
to new information, whether good or bad. If over-reaction behavior occurs, profitable
contrarian trading strategies, buying past losers and selling past winners can be formed. Smirlock
and Starks (1986) report the negative Monday effect in stock returns has been "moving up" in
time. Johnston, Kracaw, and McConnell find similar results (for GNMAs, this effect occurs after
December 1984. For T-bonds, the negative Wednesday occurs before January 1981) of Gay and
Kim (1987) and Chang and Kim (1988), who document the disappearance of Monday effects in
the commodities futures index.
III. Data and Methodology
The data in this research is taken from PC QUOTES for stocks of three US indices: Dow Jones
Industrial Average, S&P 100, and S&P 500 respectively. Stocks of each of these indices are
analyzed from the period beginning 1970 to the end of 2001. We sort the stock price data for
each stock based on calendar month and option months and estimate two different monthly
returns and standard deviations respectively. We then organize times series of monthly returns
and standard deviation of each stock according to month. For example, we pool all monthly
returns of January (only) from 1970 to 2001 and averaged over this period to estimate mean
January return for a stock. Similarly mean monthly return for all other months are estimated.
This process is followed to estimate mean monthly return for both calendar month and option
month. Then a cross section of all stocks’ monthly returns are pooled together to conduct
different econometric analysis.
Once the data are processed and pooled, we first test for the equality of mean return and
volatility for calendar month and option month, i.e., for return, if the mean calendar month
January returns is equal to mean option month January return and so on. Our hypothesis is that
there is no difference between the return and volatilities of these two types of months. Second,
we also test if there is any seasonality in monthly return. We conduct the seasonality test on both
calendar month and option month returns. Our test hypothesis would be that there is no
difference between returns in different option months. Using the F test we investigate if the
seasonality persists in return pattern of the option months. If the calendar month and option
month returns are not the same then the second issue is of our importance. Third, if seasonality
exists in options month then we investigate if one can capture abnormal returns from the
seasonality in options month by applying some trading rules.
IV. Econometric Analysis:
We propose that the mean return of the calendar month return and option month return are equal.
So our formal hypothesis is:
a OM CM
OM CM
H
H
µ µ
µ µ
?
=
:
: 0 (1)
Here, OM µ indicates the mean option month return, and CM µ indicates the calendar month mean
return. This hypothesis is tested under two different circumstances: when the variance of
calendar month and option month are same and when they are different. Similarly, the equality
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
593
of monthly variance is tested to see if the variance based on option month is equal to that of
calendar month. So, our test hypothesis is of the following form:
2 2
2 2
0
:
:
a OM CM
OM CM
H
H
s s
s s
?
=
(2)
Next we investigate the seasonality in monthly returns on both calendar month return and on
option month return. The regression equation that addresses this issue is as follows:
t t t t t t t R =a +ß D +ß D +ß D +ß D + - - - - - - - +ß D +e 1 1 2 2 3 3 4 4 11 11 (3)
The dependent variable, t R is the stock’s monthly return at time t, t
e is the white noise error
term. a , the constant, in the right hand side of the equation identifies the monthly return for the
month of January. The seasonal dummy variables are defined by the t t D1 ,.........,D12, where
??
?
??
?
=
otherwise
for the ith month
D i t 0,
1,
and month begins from the second month ( February) of the
year and hence i = 2, ------,12, indicates the difference in return between January and the ith
month of the year.
V. Results:
V.1 Summary Statistics:
We calculate returns on calendar month and option month and is presented in Table 1. Table 1A
shows the calendar month based monthly returns for the 30 DOW components from period 1970-
2001. Results are pooled by the month. It is shown in the table that on an average the DOW
components offer the lowest return of -0.686% in the month of September and highest return of
2.89% in the month of January.
Please insert Table 1A and 1B about here
Looking at the results one would presume that historically, September and August are the two
worst months for DOW components and January, December, and November are the best months.
In Table 1B it is observed that option period average monthly returns offer different returns. The
worst average return for the DOW components comes in October option month with -0.118%
and the highest average return comes in January option month with 2.602%. Historically,
October and September offer the worst returns and January, November, December, and February
offer the best returns.
Table 2A offers the summary statistics of historical returns for S&P 100 components based on
the calendar month. Results show that S&P 100 components offer worst return in the month of
April with average return of -3.747% and best return in the month of January with average return
of 3.268%.
Please insert Table 2A and 2B about here
Table 2B shows the summary statistics of historical returns for S&P 100 components based on
the option month. On average, S&P 100 components offer worst return in the option month of
October with average return of -0.051% and best return in the option month of January with
average return of 2.823%.
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
594
Table 3A presents the summary results of historical average returns for S&P 500 components
based on calendar month. It shows that April calendar month offers the worst average ret urn of -
6.937% and January calendar month offers the highest average return of 3.449%.
Please Insert Table 3A and 3B about here
When data are arranged based on option months, the summary results are shown in Table 3B. It
shows that the lowest return comes in the October option month with average return of 0.102%
and the highest return comes in the option month of January with average return of 4.078%.
V.2 Results for Mean-Variance Equality Test
Table 4-6 shows the test results on S&P 100 components. When the mean returns, based on
calendar month and option months, are tested they are done under two different assumptions:
once it is tested assuming the variances (based on calendar month and option month respectively)
are equal and next when assumed that variances are not equal.
Please Insert Table 4, 5, and 6 about here
In both cases our test results show that the null hypothesis is rejected implying that the mean
return based on calendar month and that of option month are different. Next we conduct a
variance equality test and result indicates that even variance calculated based on calendar month
and on option month is different.
We conduct the similar tests on the DOW 30 components. Table 7, 8, and 9 show the test results.
When assumed that the variance calculated based on calendar month and option month are equal,
our mean equality test indicate the similar result that we find in S&P 100 components: the means
are not equal.
Please Insert Table 7, 8, and 9 About Here
Also, when assumed the variances are not equal, the mean returns turn out to be different. The
tests for variance equality also show that the variances are not equal either.
Please Insert Table 10, 11, and 12 About Here
Finally, we conduct the similar tests on S&P 500 components. The test results are identical to
those of S&P 100 and DOW 30 Components.
The main conclusion we can draw from our findings is that monthly returns show a pattern when
they are estimated based on the option period. Outstanding options contracts, whose value
depend on the third Friday’s closing price, whether they are worthless or not and hence exercised
or not may have some impact on the closing price of the Third Friday and that may make the
monthly return and variances to be different from calendar month.
V. 3 Results for Seasonality Test
Seasonality tests are done both on calendar month return and on option month returns. Table 13
shows the seasonality test results conducted on S&P 500 components.
Please Insert Table 13 and 14 About Here
Results indicate that there is a seasonal pattern exists in the calendar month returns. Table 14
shows the seasonality test results of option month returns. It is quite clear that there is a seasonal
pattern in return structure of the option month as well. One difference we like to include is the T
statistics for the month of December. Calendar month show that it is insignificant where as the
Option month based T statistics shows that it is significant.
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
595
Table 15 and 16 show the seasonality test results for Calendar month and option month
respectively on S&P 100 components. Both calendar month and option month based tests show
that there is a seasonal pattern in return structure.
Please Insert Table 15 and 16 About Here
One interesting observation is that in calendar month analysis, December month return turns out
to be insignificant. However, in the option month based analysis it is observed that February,
May, June and October turn insignificant.
Table 17 and 18 show the seasonality test results of calendar month and option month based
returns on DOW 30 components. Results and conclusions are very similar to the ones we observe
in S&P 500 and S&P 100 components.
Please Insert Table 17 and 18 About Here
Just like S&P 500 and S&P 100, the December calendar month return comes insignificant in
DOW 30 Securities as well. When option month based test is conducted, February, October, and
November month returns turn insignificant.
VI. Concluding Remarks
We investigate whether option month based monthly returns and volatilities are different from
those of calendar month. Our test results support the differences. These differences may explain
the worthiness of options contracts on call and put options when they expire. As a result, we
conclude the option expiration effect may explain the differences in return in option month
compared to the calendar month. Our result contributes to the existing literature by offering
evidence of return differences in option month what support the option expiration effect. When
security returns are analyzed based on calendar month and option month to test for seasonality,
our results support the findings of the existing literature in terms of the calendar month. Our
findings contribute to the literature by adding the result that when security returns are analyzed
based on the option month; it also shows the seasonal pattern. Our both results could be the
added evidence against the weak-form market efficiency.
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International Research Journal of Applied Finance ISSN 2229 – 6891
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598
Summary Table 1: Calendar month Vs. Option Month Returns (Descriptive Statistics) for DJIA Components.
Calendar
Month: 1A
Jan Feb
Marc
h April May June July
Augu
st Sept Oct Nov Dec
Average
2.890
%
0.721
%
1.080
%
1.171
%
1.298
%
0.736
%
0.728
%
-
0.067
%
-
0.686
%
0.576
%
1.543
%
2.258
%
Max
11.22
5%
3.687
%
4.756
%
5.300
%
5.701
%
5.068
%
3.019
%
2.854
%
4.688
%
5.404
%
5.426
%
6.807
%
Min
-
0.549
%
-
1.883
%
-
1.130
%
-
3.920
%
-
1.592
%
-
1.845
%
-
3.459
%
-
2.180
%
-
3.170
%
-
4.073
%
-
1.730
%
-
0.135
%
Median
0.086
%
0.014
%
0.022
%
0.074
%
0.032
%
0.025
%
0.020
%
0.016
%
0.030
%
0.038
%
0.025
%
0.029
%
Variance
0.086
%
0.014
%
0.022
%
0.074
%
0.032
%
0.025
%
0.020
%
0.016
%
0.030
%
0.038
%
0.025
%
0.029
%
STDEV
2.939
%
1.201
%
1.471
%
2.729
%
1.776
%
1.591
%
1.405
%
1.274
%
1.732
%
1.949
%
1.582
%
1.710
%
Option
Month: 1B
Jan Feb
Marc
h April May June July
Augu
st Sep Oct Nov Dec
Average
2.602
%
2.123
%
1.361
%
1.096
%
1.251
%
1.419
%
0.930
%
0.904
%
-
0.012
%
-
0.118
%
2.217
%
2.178
%
Max
10.38
3%
5.423
%
5.977
%
6.330
%
4.462
%
5.134
%
5.821
%
4.746
%
5.099
%
4.515
%
6.101
%
5.640
%
Min
-
1.006
%
-
1.445
%
-
1.725
%
-
2.646
%
-
0.620
%
-
2.415
%
-
3.093
%
-
4.770
%
-
4.186
%
-
3.497
%
-
0.440
%
-
1.602
%
Median
0.070
%
0.025
%
0.027
%
0.032
%
0.018
%
0.027
%
0.037
%
0.036
%
0.035
%
0.057
%
0.021
%
0.028
%
Variance
0.070
%
0.025
%
0.027
%
0.032
%
0.018
%
0.027
%
0.037
%
0.036
%
0.035
%
0.057
%
0.021
%
0.028
%
STDEV
2.643
%
1.566
%
1.633
%
1.778
%
1.338
%
1.645
%
1.926
%
1.886
%
1.866
%
2.393
%
1.461
%
1.673
%
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
599
Summary Table 2: Calendar month Vs. Option Month Returns (Descriptive Statistics) for S&P 100 Components.
S&P
100
Calen
dar 2A
Jan Feb Mar April May June July Aug Sep Oct Nov Dec
Avg
3.268
%
0.629
%
1.409
%
-
3.747
%
1.391
%
1.028
%
0.609
%
0.233
%
-
0.142
%
1.232
%
1.339
%
2.478
%
Max
11.89
4%
6.842
%
11.62
5%
5.300
%
12.798
%
22.480
%
6.713
%
11.96
0%
8.798
%
12.232
%
10.34
5%
27.019
%
Min
-
2.925
%
-
10.500
%
-
9.042
%
-
55.806
%
-
24.162
%
-
20.222
%
-
7.087
%
-
7.813
%
-
16.427
%
-
11.067
%
-
7.942
%
-
12.697
%
Med
2.698
%
0.753
%
1.113
%
-
1.942
%
1.327
%
0.639
%
0.826
%
0.009
%
-
0.337
%
1.043
%
1.313
%
2.075
%
Var
0.101
%
0.065
%
0.065
%
0.844
%
0.145
%
0.184
%
0.049
%
0.070
%
0.096
%
0.109
%
0.065
%
0.193
%
STDE
V
3.171
%
2.549
%
2.545
%
9.186
%
3.804
%
4.295
%
2.217
%
2.642
%
3.093
%
3.294
%
2.547
%
4.397
%
Optio
n: 2B
Jan Feb Mar April May June July Aug Sep Oct Nov Dec
Avg
2.823
%
2.359
%
1.937
%
0.499
%
2.031
%
2.058
%
1.261
%
0.973
%
0.782
%
-
0.051
%
2.767
%
1.685
%
Max
16.87
4%
11.038
%
17.02
3%
6.330
%
31.250
%
22.070
%
12.95
4%
6.748
%
6.967
%
6.543
%
16.02
5%
24.713
%
Min
-
5.283
%
-
5.712
%
-
7.887
%
-
10.623
%
-
3.781
%
-
23.223
%
-
8.158
%
-
7.878
%
-
8.934
%
-
13.032
%
-
4.133
%
-
14.426
%
Med
2.115
%
2.283
%
1.726
%
0.599
%
1.361
%
1.595
%
0.865
%
0.965
%
0.580
%
-
0.008
%
2.278
%
1.607
%
Var
0.151
%
0.060
%
0.078
%
0.069
%
0.143
%
0.183
%
0.091
%
0.049
%
0.060
%
0.095
%
0.101
%
0.128
%
STDE
V
3.880
%
2.444
%
2.793
%
2.621
%
3.784
%
4.276
%
3.013
%
2.206
%
2.443
%
3.079
%
3.185
%
3.583
%
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
600
Summary Table 3: Calendar month Vs. Option Month Returns (Descriptive Statistics) for S&P 500 Components.
S&P
500
Calendar 3A
Jan Feb Mar April May June July Aug Sep Oct Nov Dec
Avg
3.44
9%
0.933
%
1.444
%
-
6.937
%
1.907
%
1.371
%
0.449
%
1.040
%
0.330
%
1.672
%
1.314
%
3.43
4%
Max
51.8
25%
32.608
%
22.11
7%
5.300
%
24.390
%
48.326
%
18.34
2%
38.133
%
22.939
%
27.535
%
39.74
0%
41.6
80%
Min
-
9.14
8%
-
22.402
%
-
39.99
3%
-
10.000
%
-
33.424
%
-
22.287
%
-
27.48
5%
-
15.169
%
-
21.596
%
-
11.067
%
-
24.01
1%
-
12.6
97%
Median
2.33
5%
0.730
%
1.301
%
-
3.887
%
1.719
%
0.591
%
0.555
%
0.306
%
0.090
%
0.989
%
1.312
%
2.61
8%
Variance
0.31
2%
0.146
%
0.216
%
1.530
%
0.199
%
0.343
%
0.147
%
0.231
%
0.182
%
0.207
%
0.152
%
0.23
7%
Option: 3B
Jan Feb Mar April May June July Aug Sep Oct Nov Dec
Avg
4.07
8%
2.604
%
1.741
%
1.143
%
2.787
%
2.305
%
2.330
%
1.103
%
1.223
%
0.102
%
3.435
%
1.57
9%
Max
35.3
10%
38.301
%
35.87
6%
20.866
%
55.573
%
60.900
%
84.66
6%
23.258
%
18.630
%
41.102
%
52.42
1%
33.2
09%
Min
-
13.3
49%
-
26.859
%
-
17.37
3%
-
29.641
%
-
27.673
%
-
37.671
%
-
15.12
7%
-
21.465
%
-
14.435
%
-
16.733
%
-
11.73
8%
-
24.0
61%
Median
2.81
2%
2.354
%
1.445
%
1.113
%
1.769
%
1.773
%
1.308
%
0.937
%
0.724
%
0.041
%
2.487
%
1.47
2%
Variance
0.31
9%
0.189
%
0.185
%
0.168
%
0.300
%
0.303
%
0.414
%
0.141
%
0.142
%
0.163
%
0.294
%
0.19
2%
4.304
%
4.104
%
5.473
%
5.507
%
6.437
%
3.756
%
3.763
%
4.037
%
5.423
%
4.37
9%
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
601
Table 4
Table 5
T-Test: Two-Sample Assuming Equal Variances S&P 100
Variable 1 Variable 2
Mean 0.007975726 0.015796377
Variance 0.001893774 0.001068701
Observations 1224 1224
Pooled Variance 0.001481237
Hypothesized Mean Difference 0
Df 2446
t Stat -5.02696988
P(T
t Critical one-tail 1.645476829
P(T
t Critical two-tail 1.960934263
T-Test: Two-Sample Assuming Unequal Variances S&P 100
Variable 1 Variable 2
Mean 0.007975726 0.015796377
Variance 0.001893774 0.001068701
Observations 1224 1224
Hypothesized Mean Difference 0
Df 2270
t Stat -5.02696988
P(T
t Critical one-tail 1.645525167
P(T
t Critical two-tail 1.961009535
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
602
Table 6
Table 7
Table 8
T-Test: Two-Sample Assuming Unequal Variances Dow 30
Variable 1 Variable 2
Mean 0.009950414 0.013292335
Variance 0.00040674 0.000390124
Observations 360 360
Hypothesized Mean Difference 0
df 718
t Stat -2.246235835
P(T
t Critical one-tail 1.646978626
P(T
t Critical two-tail 1.963273425
T-Test: Two-Sample Assuming Equal Variances Dow 30
Variable 1 Variable 2
Mean 0.009950414 0.013292335
Variance 0.00040674 0.000390124
Observations 360 360
Pooled Variance 0.000398432
Hypothesized Mean Difference 0
Df 718
t Stat -2.246235835
P(T
t Critical one-tail 1.646978626
P(T
t Critical two-tail 1.963273425
F-Test Two-Sample for Variances S&P 100
Variable 1 Variable 2
Mean 0.007975726 0.015796377
Variance 0.001893774 0.001068701
Observations 1224 1224
Df 1223 1223
F 1.772033996
P(F
F Critical one-tail 1.098675144
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
603
Table 9
Table 10
T-Test: Two-Sample Assuming Equal Variances S&P 500
Variable 1 Variable 2
Mean 0.008750693 0.020359568
Variance 0.004248011 0.002667552
Observations 6000 6000
Pooled Variance 0.003457782
Hypothesized Mean Difference 0
Df 11998
t Stat -10.81314392
P(T
t Critical one-tail 1.644980639
P(T
t Critical two-tail 1.960161671
T-Test: Two-Sample Assuming Equal Variances S&P 500
Table 11
T-Test: Two-Sample Assuming Unequal Variances S&P 500
Variable 1 Variable 2
Mean 0.008750693 0.020359568
Variance 0.004248011 0.002667552
Observations 6000 6000
Hypothesized Mean Difference 0
Df 11402
t Stat -10.81314392
P(T
t Critical one-tail 1.644987279
P(T
t Critical two-tail 1.960172008
T-Test: Two-Sample Assuming Unequal Variances
F-Test Two-Sample for Variances Dow 30
Variable 1 Variable 2
Mean 0.009950414 0.013292335
Variance 0.00040674 0.000390124
Observations 360 360
Df 359 359
F 1.042592014
P(F
F Critical one-tail 1.189882153
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
604
Table 12
F-Test Two-Sample for Variances S&P 500
Variable 1 Variable 2
Mean 0.008750693 0.020359568
Variance 0.004248011 0.002667552
Observations 6000 6000
df 5999 5999
F 1.592475574
P(F
F Critical one-tail 1.043391895
Table 13: Calendar Month Seasonality Test on S&P 500 Components (1970-2001)
Regression Statistics
Multiple R 0.399606
R Square 0.159685
Adjusted R
Square 0.158056
Standard Error 0.058236
Observations 5688
ANOVA
df SS MS F
Significance
F
Regression 11 3.657979 0.332544 98.0553 6.4E-205
Residual 5676 19.24952 0.003391
Total 5687 22.9075
Coefficients
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 0.034489 0.002675 12.89373 1.63E-37 0.029245 0.039733 0.029245 0.039733
February -0.02516 0.003783 -6.65084 3.19E-11 -0.03257 -0.01774 -0.03257 -0.01774
March -0.02005 0.003783 -5.29967 1.2E-07 -0.02746 -0.01263 -0.02746 -0.01263
April -0.10386 0.003783 -27.455 6.3E-156 -0.11127 -0.09644 -0.11127 -0.09644
May -0.01542 0.003783 -4.07577 4.65E-05 -0.02283 -0.008 -0.02283 -0.008
June -0.02077 0.003783 -5.49171 4.15E-08 -0.02819 -0.01336 -0.02819 -0.01336
July -0.02999 0.003783 -7.92924 2.64E-15 -0.03741 -0.02258 -0.03741 -0.02258
August -0.02409 0.003783 -6.36758 2.07E-10 -0.0315 -0.01667 -0.0315 -0.01667
September -0.03119 0.003783 -8.24583 2.02E-16 -0.03861 -0.02378 -0.03861 -0.02378
October -0.01777 0.003783 -4.69778 2.69E-06 -0.02519 -0.01036 -0.02519 -0.01036
November -0.02134 0.003783 -5.64248 1.76E-08 -0.02876 -0.01393 -0.02876 -0.01393
December -0.00014 0.003783 -0.03803 0.969664 -0.00756 0.007272 -0.00756 0.007272
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
605
Table 14: Option Month Seasonality Test on S&P 500 Components (1970-2001)
Regression Statistics
Multiple R 0.212326
R Square 0.045082
Adjusted R
Square 0.043232
Standard
Error 0.048935
Observations 5688
ANOVA
df SS MS F
Significance
F
Regression 11 0.64169 0.058335 24.36078 8.37E-50
Residual 5676 13.59201 0.002395
Total 5687 14.2337
Coefficients
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 0.040777 0.002248 18.14184 1.52E-71 0.03637 0.045183 0.03637 0.045183
February -0.01474 0.003179 -4.63689 3.62E-06 -0.02097 -0.00851 -0.02097 -0.00851
March -0.02337 0.003179 -7.35074 2.25E-13 -0.0296 -0.01713 -0.0296 -0.01713
April -0.02935 0.003179 -9.23296 3.64E-20 -0.03558 -0.02312 -0.03558 -0.02312
May -0.0129 0.003179 -4.05982 4.98E-05 -0.01914 -0.00667 -0.01914 -0.00667
June -0.01773 0.003179 -5.57692 2.56E-08 -0.02396 -0.0115 -0.02396 -0.0115
July -0.01748 0.003179 -5.49921 3.98E-08 -0.02371 -0.01125 -0.02371 -0.01125
August -0.02975 0.003179 -9.35851 1.14E-20 -0.03598 -0.02352 -0.03598 -0.02352
September -0.02855 0.003179 -8.98065 3.6E-19 -0.03478 -0.02232 -0.03478 -0.02232
October -0.03976 0.003179 -12.5076 1.98E-35 -0.04599 -0.03353 -0.04599 -0.03353
November -0.00642 0.003179 -2.02076 0.043352 -0.01265 -0.00019 -0.01265 -0.00019
December -0.02499 0.003179 -7.86114 4.52E-15 -0.03122 -0.01876 -0.03122 -0.01876
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
606
Table 15: Calendar Month Seasonality Test on S&P 100 Components (1970-2001)
Regression Statistics
Multiple R 0.373864
R Square 0.139774
Adjusted R
Square 0.131967
Standard Error 0.040545
Observations 1224
ANOVA
Df SS MS F
Significance
F
Regression 11 0.323729 0.02943 17.90293 2.29E-33
Residual 1212 1.992357 0.001644
Total 1223 2.316085
Coefficients
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 0.032678 0.004015 8.139874 9.73E-16 0.024801 0.040554 0.024801 0.040554
February -0.02643 0.005677 -4.65586 3.58E-06 -0.03757 -0.01529 -0.03757 -0.01529
March -0.01872 0.005677 -3.29796 0.001002 -0.02986 -0.00759 -0.02986 -0.00759
April -0.06988 0.005677 -12.308 6.89E-33 -0.08102 -0.05874 -0.08102 -0.05874
May -0.01886 0.005677 -3.32226 0.00092 -0.03 -0.00772 -0.03 -0.00772
June -0.02247 0.005677 -3.95758 8.01E-05 -0.03361 -0.01133 -0.03361 -0.01133
July -0.02687 0.005677 -4.73295 2.47E-06 -0.03801 -0.01573 -0.03801 -0.01573
August -0.03034 0.005677 -5.3443 1.08E-07 -0.04148 -0.0192 -0.04148 -0.0192
September -0.03433 0.005677 -6.04619 1.97E-09 -0.04547 -0.02319 -0.04547 -0.02319
October -0.02091 0.005677 -3.68301 0.000241 -0.03205 -0.00977 -0.03205 -0.00977
November -0.01946 0.005677 -3.42755 0.000629 -0.0306 -0.00832 -0.0306 -0.00832
December -0.00815 0.005677 -1.43548 0.151409 -0.01929 0.002989 -0.01929 0.002989
Table 16: Option Month Seasonality Test on S&P 100 Components (1970-2001)
Regression Statistics
Multiple R 0.266395
R Square 0.070966
Adjusted R
Square 0.062535
Standard Error 0.031652
Observations 1224
ANOVA
df SS MS F
Significance
F
Regression 11 0.092755 0.008432 8.416493 2.05E-14
Residual 1212 1.214266 0.001002
Total 1223 1.307021
Coefficients
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 0.02823 0.003134 9.007508 7.99E-19 0.022081 0.034379 0.022081 0.034379
Intercept -0.00467 0.004432 -1.0546 0.29182 -0.01337 0.004021 -0.01337 0.004021
February -0.00913 0.004432 -2.06043 0.039571 -0.01783 -0.00044 -0.01783 -0.00044
March -0.02303 0.004432 -5.1969 2.38E-07 -0.03173 -0.01434 -0.03173 -0.01434
April -0.008 0.004432 -1.80533 0.07127 -0.0167 0.000694 -0.0167 0.000694
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
607
May -0.00789 0.004432 -1.78018 0.075297 -0.01659 0.000806 -0.01659 0.000806
June -0.01582 0.004432 -3.56977 0.000371 -0.02452 -0.00713 -0.02452 -0.00713
July -0.01845 0.004432 -4.16317 3.36E-05 -0.02715 -0.00976 -0.02715 -0.00976
August -0.02063 0.004432 -4.65563 3.59E-06 -0.02933 -0.01194 -0.02933 -0.01194
September -0.02919 0.004432 -6.58529 6.75E-11 -0.03788 -0.02049 -0.03788 -0.02049
October -0.00083 0.004432 -0.18808 0.850844 -0.00953 0.007862 -0.00953 0.007862
November -0.01154 0.004432 -2.60393 0.009329 -0.02024 -0.00285 -0.02024 -0.00285
Table 17: Calendar Month Seasonality Test on DJIA Components (1970-2001)
Regression Statistics
Multiple R 0.445377
R Square 0.198361
Adjusted R
Square 0.173021
Standard Error 0.01834
Observations 360
ANOVA
df SS MS F
Significance
F
Regression 11 0.028965 0.002633 7.828214 3.73E-12
Residual 348 0.117055 0.000336
Total 359 0.14602
Coefficients
Standard
Error t Stat P-value Lower 95%
Upper
95%
Lower
95.0%
Intercept 0.028899 0.003348 8.630455 2.22E-16 0.022313 0.035484 0.022313
February -0.02248 0.004735 -4.74801 3.01E-06 -0.0318 -0.01317 -0.0318
Upper
95.0%
March -0.0181 0.004735 -3.8214 0.000157 -0.02741 -0.00878 -0.02741 0.035484
April -0.01878 0.004735 -3.96553 8.89E-05 -0.02809 -0.00946 -0.02809 -0.01317
May -0.01634 0.004735 -3.45092 0.000627 -0.02566 -0.00703 -0.02566 -0.00878
June -0.0214 0.004735 -4.51823 8.55E-06 -0.03071 -0.01208 -0.03071 -0.00946
July -0.02147 0.004735 -4.53375 7.98E-06 -0.03078 -0.01216 -0.03078 -0.00703
August -0.02957 0.004735 -6.24521 1.23E-09 -0.03889 -0.02026 -0.03889 -0.01208
September -0.03532 0.004735 -7.45842 7.04E-13 -0.04463 -0.02601 -0.04463 -0.01216
October -0.02312 0.004735 -4.88261 1.6E-06 -0.03243 -0.01381 -0.03243 -0.02026
November -0.01425 0.004735 -3.00873 0.002815 -0.02356 -0.00493 -0.02356 -0.02601
December -0.00655 0.004735 -1.38382 0.1673 -0.01587 0.002761 -0.01587 -0.01381
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
608
Table 18: Option Month Seasonality Test on DJIA Components (1970-2001)
Regression Statistics
Multiple R 0.40731
R Square 0.165901
Adjusted R
Square 0.139536
Standard Error 0.018322
Observations 360
ANOVA
df SS MS F
Significance
F
Regression 11 0.023235 0.002112 6.292434 1.71E-09
Residual 348 0.116819 0.000336
Total 359 0.140054
Coefficients Standard Error t Stat P-value Lower 95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 0.026024 0.003345 7.779672 8.35E-14 0.019445 0.032603 0.019445 0.032603
February -0.00538 0.004731 -1.13676 0.256423 -0.01468 0.003927 -0.01468 0.003927
March -0.01225 0.004731 -2.58968 0.01001 -0.02156 -0.00295 -0.02156 -0.00295
April -0.01459 0.004731 -3.08408 0.002205 -0.02389 -0.00529 -0.02389 -0.00529
May -0.01352 0.004731 -2.85787 0.004522 -0.02282 -0.00422 -0.02282 -0.00422
June -0.01206 0.004731 -2.54976 0.011208 -0.02137 -0.00276 -0.02137 -0.00276
July -0.01698 0.004731 -3.58983 0.000378 -0.02629 -0.00768 -0.02629 -0.00768
August -0.01685 0.004731 -3.56278 0.000418 -0.02616 -0.00755 -0.02616 -0.00755
September -0.02577 0.004731 -5.44808 9.65E-08 -0.03508 -0.01647 -0.03508 -0.01647
October -0.02697 0.004731 -5.70123 2.54E-08 -0.03627 -0.01767 -0.03627 -0.01767
November -0.00406 0.004731 -0.85853 0.391189 -0.01337 0.005243 -0.01337 0.005243
December -0.00433 0.004731 -0.91622 0.360188 -0.01364 0.00497 -0.01364 0.00497
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Determinants Influencing the Seasoned Equity Offerings: Private
Placements vs Rights Issue
Norhanim Dewa
Taylor’s Business School
Taylor’s University
Selangor, Malaysia
[email protected]
Izani Ibrahim
Graduate School of Business
National University of Malaysia
Selangor, Malaysia
[email protected]
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610
Abstract
The number of private placements proposals demonstrates an interesting change and it is
steadily being made use of as one of the equity financing method for the last 15 years in
Malaysia. The volume of private placements in these years significantly rises as well
comparatively to the same range of period. However the number of rights issue proposals
demonstrates the opposite pattern of private placements similarly to the volume. The increase
of private placements combine with the decrease of rights issue within the same period
initiate the need to identify the existence of either differences or similarities in the
determinants of firms that chose these financing methods. It is found that there are
differences in the determinants of firms that chose private placement with firms that chose
rights issue. The findings report that the determinants of firms that chose private placements
are with high degree of asymmetric information, limited financial slack undervalued as well
as large. And the determinants of firms that chose rights issue shares are high degree of
asymmetric information, limited financial slack and large. The diversity of findings confirms
that the determinants might be caused by the characteristics in emerging markets compared to
established environments.
Keywords: private placement, rights issue, determinants, seasoned equity offerings, Malaysia
JEL: G30, G32
I. Introduction
The pecking order theory states that firm chooses to issue equity as the last resort to raise
funds may perhaps be explained by the finding of these fellow researchers. It is suggested
that the public perceived the issuance of seasoned equity offering as due to limited capability
of firms in raising either internal funds or failure to raise funds via lending. Nonetheless firms
discover that it is not easy to avoid equity issuance particularly when the financing is required
for either investment or growth opportunities. Hence fundamentally firms have two options to
raise fund via equity which is via the open market viz. bonus issue, stock split, rights issue
and seasoned offerings or via the private market viz. private placement.
Past studies of equity issuance corroborate that firms which opt for public offerings encounter
negative results and confirm the least preference of financing in pecking order theory. Studies
performed by Mikkelson and Ruback (1985), Schleifer and Vishny (1986), Agrawal and
Mandelker (1990) and Brous and Kini (1994) show that public offerings are seen to have a
negative association with shareholders wealth. Furthermore studies performed by Healy and
Palepu (1990) and Jain (1992) illustrate that market will construe public offerings as an
indication that the firm is in need of cash or dealing with financial problems which causes the
negative return upon the announcement of public offerings.
In contrast to the issuance of public offerings as discussed above funds that are raised via
private market is dependent on merely the scrutiny of the intended parties that has direct
contact with the managers as well as shareholders of the firms. The divergence in the process
of public issuance and private issuance may lead to the variation of motivations to choose
either method.
The circumstances are substantiated with the outcome of the research performed by Wruck
(1989). The research claims that there is positive relationship between issuance of private
equity and shareholder’s wealth due to ownership concentration level. Afterward Goh,
Gombola, Lee, & Liu (1999) prove that private placements have the capability to convey
special information to the market that lead to positive wealth impact. This is again being
supported by the research of Lee and Kocher (2001) and Brooks and Graham (2005) that
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stated firm size and ownership structure are the important determinants which release the
positive information that explains the market reaction for private placement.
Nevertheless Prowse (1998) reported that the private equity market in United States has been
the fastest growing market for corporate finance in the past 15 years. Moreover Carey,
Prowse, Rea, & Udell (1993) stated that the US market of private equity issuance started to
grow in the late 1980s from a mere USD6 billion to more than USD150 billion in 1996
(Prowse, 1998). There are many published articles that discussed the private equity trends in
other countries that start as early as in 1989.
As such the list of researches in the US market continues until now and it is extended to the
Japan market by Kato and Schallheim (1993), Singapore market by Tan, Chng, & Tong,
(2002) and New Zealand market by Anderson, Rose & Cahan (2006). The pronouncement of
these studies revealed that a private placement is capable to reverse the impact of public
offerings for most of the market except for Singapore market. Though the reverse in wealth
impact may possibly explain the reasons for change in the number of proposals and volume
in Malaysia, the researchers in Singapore market has concluded that the variations may exist
due to differences of regulation in some of the markets.
Private placements in Malaysia are perceived as a second class option of equity financing
method. This is elucidated by the volume of private placement over the period of fifteen years
from the year 1975 to 1990 is recorded at a mere RM97.8 million and occurs in three
occasions of 1976, 1978 and 1987 respectively. Subsequently private placement was steadily
being made use of from the year 1992 to 2004 with an upward trend for four consecutive
years starting in 1992 and another five consecutive years starting in 1998 with a total volume
over the same period of fifteen years recorded at RM17.8 billion.
On the other hand rights issue is a more common method of financing among Malaysian
company as the use and the volume of proceeds can be traced back to the year 1976 reported
at RM9.5 million and jumped to RM4.419 billion in 1990 for the same periods mentioned in
private placements. Nonetheless from the year 1990 to year 2009 the overall volume of rights
issues are sensitive towards economic changes as the Asian Financial Crisis caused a drop
from RM8.525 billion to RM722 million. Yet again rights issue is consistently being
employed for all the years though the volume rises up in 1999 followed by seven consecutive
years of plunging down ended in 2006. Indisputably it is a more popular method of financing
tools in Malaysia comparatively to private placements.
Owing to this a look into the information provided by SC on the number of accepted private
placement proposals as well as the number of rights issue proposals may perhaps indicate the
current trend of corporate raising exercises as well as the recent change of pattern over the
issuance of private placement proposals in Malaysia.
[Insert Table 1 here]
II. Literature Review
Free Cash Flow Hypothesis
Jensen (1986) defines free cash flow as the excess cash flow required to fund projects with
positive net present value that improve the future expected return of firms. Copeland and
Weston (1988) state that the distributions of excess cash flow to existing shareholders reduce
the amount of funds under managers’ control. Lesser management control reduces their
power on future growth opportunities that once again under the scrutiny of the market when
firms decide to seek for additional funds particularly in raising new capital. Hertzel and
Smith (1993) in an extension of Myers and Majluf (1984) argue that private placement is
used by firms with limited financial slack or free cash flow in order to take advantage of
profitable investment opportunities. Brooks and Graham (2005) corroborate the outcome of
others that firms with low excess cash flow may be inclined to issue equity through the
private transaction rather than the public transaction. Despite this Brooks and Graham (2005)
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as well state that established firms with excess cash flow problem are associated with public
offerings. Hence it is propose that free cash flow and private placements are inversely related
while free cash flow and rights issues are positively related.
Asymmetric Information Hypothesis
The finding of Akerlof (1970) led to the work of Leland and Pyle (1977) that linked the role
of information with the financial structure of a firm. The link is being established through the
action of entrepreneurs as one of the indicators for the type of information being dispersed in
the market. Their argument on choosing the action of entrepreneur as an indicator is based on
the belief that as a shareholder entrepreneur act to protect his interest upon the release of any
information to the market. The research of Myers and Majluf (1984) reveal that the level of
information possessed by managers’ is superior to the level of information possessed by
investors. The work discussed the impact on level of information towards the investment and
financing decisions of managers. Hertzel and Smith (1993) initiate that the private sales of
equity carry important information effect while Anderson (2006) suggests that the
informational effect of private placement in a lesser regulated market documented that private
placement is a tool used by firms to exploit the market. In contrast to this Lee and Kocher
(2001) illustrate that public common stock is not chosen if firms suffers greater degree of
asymmetric information since the issuance diffuse negative information to the market. It is
propose that asymmetric information and private placements are positively related while
asymmetric information and rights issues are inversely related.
Stock Price Run-up Hypothesis
Charles Dow, the founder of Dow Jones as cited by Hamilton (1922) defines the up-trends as
a time when there is successive rallies price that close at a level higher than those achieved in
the previous rallies and when lows occur at a level higher than the previous lows.
Nonetheless the links between stock price run-up theories with the issuance of equity is
ascertained through the overvaluation theory that suggested firms sell their shares when the
market price is superior to the book price in order to avoid extensive pull down of prices.
Thus Hertzel and Smith (1993) suggest that as long as the NPV of investment is higher than
the cost of transferring information, managers of undervalued firm chose private placement
as their financing method in order to mitigate under-investment problem that is supported by
Lee and Kocher (2001). Meanwhile Hertzel, Lemmon, Linck & Rees (2002) confirm the
evidence of investor optimism surrounding the announcement of private placement is
attributable to the significant price run-up. On the contrary Lee and Wu (2009) state that the
implication of asymmetric information causes firms to issue public offerings when the shares
are overvalued which affirmed to the finding of Myers and Majluf (1984). Hence stock price
run-up and private placements are inversely related while stock price run-up and rights issues
are positively related.
Agency Cost Hypothesis
Jensen and Meckling (1976) has develop a theory of ownership structure with relevant to the
theory of agency, property rights and finance. Agency theory as explained by Jensen and
Meckling (1976) affirm that the costs of the relationship between agent and principal arise
due to the contract drawn to ensure the action of agent is consistent with the interest of
principal. Hence Lehn and Poulsen (1989) suggest that the monitoring effect as well as the
alignment of managers and shareholders interest reduce the severity of agency costs in a firm.
Wruck (1989) establish that private placement instituted the change on the level of ownership
concentration which creates a new block-holder of share that enable the alignment of
managers and shareholders interest through supervision and monitoring. Cronqvist and
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Nilsson (2005) institute that in addition to monitoring effect, private placement is useful to
reduce moral hazard particularly to align interest between firms and business partners as well
as when the potential of value-reducing managerial discretion is high. Despite this Wu, Wang
& Yao (2005) ascertain that public offerings do not lead to monitoring effect due to the
involvement of passive investors and smaller shareholding. Based on this agency cost and
private placements are positively related while agency cost and rights issues are inversely
related.
Firm Size Hypothesis
Likewise Freeman (1987) asserts that small firms tend to experience a greater degree of
asymmetric information than large firms prior to announcement of any corporate events.
Hertzel and Smith (1993) postulates that small firm with substantial growth opportunities
tend to choose private placement in order to raise fund externally. As for Lee and Kocher
(2001) firm size is an indicator to support asymmetric information following Freeman (1987)
that suggests the asymmetric information problem is borne by small firms with investment
opportunities. Wu et. al (2005) corroborated that private equity issuers are usually small and
suffers asymmetric information. Nonetheless Lee and Kocher (2001) also conclude that
public issuance is selected by larger firms as an indicator of positive past and current
performance. Thus the proposition is firm size and private placements are inversely related
while firm size and rights issues are positively related.
III. Methodology
The sample collections focus on identifying firms that announced private placement and
rights issue. Both of these samples are collected either through Investor’s Digest or directly
from Bursa Malaysia website. The website search is classify under company announcement
and by keying in keywords such as “private placement”, “equity private placement”, “equity
right issues”, “rights issue” as well as “renounceable rights issue”. The search on the
announcements is extending to Investor’s Digest for the announcements prior to 2004. The
initial sample is match with the characteristics that only one issuance of secondary equity
offerings is announced by companies currently listed in Bursa Malaysia from the period of
January 2002 to December 2007 and the announcements are not from the same companies.
The final number of sample that is pursued for collection of data is 118 companies for private
placements issuers and 114 companies for rights issue issuers.
The extraction of measurable data is performed in Data stream and Perfect Analysis though it
is expanded to Bursa Malaysia website for downloading of firms’ annual report. The
dependent variable selected to reflect the choice of methods is the natural log of gross
proceeds for each issuance. The gross proceeds are measured by multiplying the number of
issued shares with the issue price on the day of announcement that is used by Wu (2001) and
Tan et al. (2002). The measurement of free cash flow by Copeland and Weston (1988)
includes the earnings before interest and tax, depreciation, change in working capital as well
as the tax impact on depreciation and interest is selected as the first independent variable and
the proxy for free cash flow hypothesis.
The second independent variable and proxy of asymmetric information theory is the
comparison between book value of equity to the market value of equity in the preceding year
of announcement referred in Chen et al. (2002), Tan et al. (2002) and Anderson et al. (2006).
The third independent variable as a proxy to stock price run-up theory is cumulative
abnormal return using market model of Scholes and William (1977) for beta estimation as in
Wruck (1989), Anderson et al. (2006) and Barclay et al. (2007). The fourth independent
variable and the proxy of agency costs theory is measured as the change in the ownership
fraction of managers including directors to the shareholders similarly to Hertzel and Smith
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(1993), Lee and Kocher (2001). The final independent variable selected as the proxy of firm
size hypothesis is the logarithm of market value of equity.
The statistical testing are White Heteroscedasticity test of homoscedasticity on the variance
of residuals and the Variance Inflation Factor (VIF) test of multicollinearity for the
independent variables. The RAMSEY Reset test of stability testing for justification of
misspecification is performed. The bi-variate correlation among independent variables as
well as between dependent and independent variable is reflected by the Pearson correlation
test. Eventually the developed model undertakes the cross section pool data regression
analysis subjected to the fitness of model testing that is reflected in F-test, t-test and adjusted
R2 result. The developed model is written as below:
LOGGPppe/ri = ß0 + ß1FCFppe/ri + ß2AIppe/ri + ß3RPppe/ri + ß4ACppe/ri + ß5FSppe/ri + e
where,
LOGGP = log of gross private placement or rights issue proceeds
FCF = free cash flow of private placement or rights issue issuer
AI = BVMV = asymmetric information of private placement or rights issue issuer
RP = CAR = stock price run-up indicator of private placement or rights issue issuer
AC = OWNFRAC = agency cost of private placement or rights issue issuer
FS = firm size of private placement or rights issue issuer
ß0 = constant
ß1,2,…5 = regression coefficients of predictor
e = error term
IV. Results
The heteroscedasticity testing of White reported in Table 2 below shows a p-value of lesser
than 0.05 for both private placements and rights issue. The results suggest the existence of
heteroscedasticity in both developed models.
[Insert Table 2 here]
The outcome of multicollinearity test of Variance Inflation Factor (VIF) is reported to fall in
the range 1.035 to 1.287. Based on the report of results in Table 3 it is concluded that there is
no serious multicollinearity in the developed models of private placement.
[Insert Table 3 here]
Table 4 reports the outcome of stability testing for the developed model of private placement
with p-value of more than 0.05. The documented p-value suggests that there is no
misspecification in the model. The outcome of RAMSEY Reset test of stability testing for the
developed model of rights issue reported in Table 4 also suggest the absence of
misspecification in the model.
[Insert Table 4 here]
Table 5 documents that there is significant positive relationship between firm size hypothesis
and log of gross proceeds for private placements issuers as well as firm size hypothesis and
free cash hypothesis. Nonetheless there is significant negative relationship between firm size
hypothesis and asymmetric information theory. Hence the outcomes suggest that firm size
hypothesis is the characteristic of private placements issuers along with the relationship of
asymmetric information theory and free cash flow hypothesis.
Table 5 reports significant relationship firm size theory and log of gross proceeds of rights
issue as positive and on top of that firm size hypothesis has significant negative relationship
with the asymmetric information theory. Additionally the asymmetric information theory has
significant positive relationship with the price run-up theory. Thus the outcome suggests that
the firm size hypothesis and asymmetric information theory is the determinants of rights issue
issuers while the asymmetric information theory is as well related to the price run-up theory.
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[Insert Table 5 here]
The outcome of pool data regression analysis with corrected heteroscedasticity for the
developed model is reported in Table 6. The analysis reports the result of R2 and adjusted R2
at 0.622 and 0.599 supported by the outcome of F-statistics with p-value of less than 0.05 that
suggest the independent variables can jointly influence the dependent variable.
The t-statistic testing in Table 6 documents significant negative relationship between free
cash flow hypothesis and the log of gross proceeds of private placement that leads to the
conclusion that limited financial slack is the determinants of private placements issuers. The
finding supports the proposed relationship of private placement but the relationship of free
cash flow with the log of gross proceeds of rights issue is reported to be similar to private
placement instead of the opposite direction as being proposed. Owing to this it is instituted
that limited financial slack is as well the determinants of rights issue issuers.
The p-value of t-statistics reports significant positive relationship between the ratios of book
to market value of equity with the log of gross proceeds of private placement support the
proposed relationship. It is suggested that high level of asymmetric information is the
determinants of private placements issuers. On top of that the relationship between the ratios
of book to market value of equity with log of gross proceeds of rights issue is found to be
positive rather than negative as stated in the proposition. Thus it is concluded that high level
of asymmetric information is as well the determinants of rights issue issuers.
The outcome of the t-statistic reports significant negative relationship between cumulative
abnormal return and the log of gross proceeds of private placements. This is interpreted as
undervaluation of share price is the determinants of private placements issuers consistent with
the proposed relationship. Nevertheless the positive relationship between cumulative
abnormal return stated in the proposed relationship of rights issue is found to be not
significant. Due to this the proposition that overvaluation of share price is the determinants of
rights issue issuers are not conclusive.
The relationship between ownership fraction and log of gross proceeds of private placement
is found to be negative as opposed to the proposition though it is not significant. The result
suggests that high agency costs as the determinants of private placements issuers are not
supported. Moreover the low agency cost as the determinants of private placements issuers is
not conclusive as well. Similarly the relationship between ownership fraction and log of gross
proceeds of rights issue is negative as proposed but statistically insignificant. Owing to this it
is concluded that high agency cost is the determinants of rights issue issuers are not
conclusive.
Finally the relationship between firm size and the log of gross proceeds of private placement
is found to be positive instead of negative as stated in the proposition. The positive
relationship is statistically significant that suggest large firms are inclined to issue private
placements. Additionally the positive relationship between firm size and log of gross
proceeds of rights issue is also statistically significant and consistent with the proposed
relationship. In lieu of this it is concluded that large firms do issue rights issuance as their
financing method.
Eventually the outcome reveals the support for the propositions as well as the contrast of the
propositions. The differences and similarities in the characteristic of firm that choose private
placements and rights issue is determine though the finding may not be as stipulated in the
propositions. But without doubt the result fulfilled the objective of this study which is to
differentiate the determinants of both issuing firms. The developed model of private
placement is written as below:
LOGGPPPE = ß0 + ß1FCFPPE + ß2AIPPE + ß3RPPPE + ß4ACPPE + ß5FSPPE + e = 6.4621 - 8.667e-
007FCF + 5.201e-008AI - 0.0199RP + 0.0742FS + e
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And the developed model of rights issue is written as below:
LOGGPRI = ß0 + ß1FCFPPE + ß2AIPPE + ß3RPPPE + ß4ACPPE + ß5FSPPE + e = 5.929- 6.019e-
008FCFri + 1.304e-007AIri + 0.3049FSri + e
[Insert Table 6 here]
V. Discussion
The finding that firms with limited free cash flow is the determinants for private placements
issuers is consistent with the finding of Hertzel and Smith (1993), Hertzel and Rees (1998),
Goh et. al (1999), Lee and Kocher (2001) as well as Brooks and Graham (2005). The
conclusions that higher degree of asymmetric information as the determinants for private
placements issuers is consistent with the finding of Hertzel and Smith (1993), Lee and
Kocher (2001), Wu (2003) and Anderson (2006) but not by Chen et. al (2002). Subsequently
it is concluded that undervalued as the determinants for private placements issuers is
similarly reported in Hertzel and Smith (1993) and Lee and Kocher (2001) that is further
supported by Hertzel et.al (2002) as well as Marciukaityte et. al (2005) with investors’
optimism as the underlying explanations. The negative relationship between agency cost
theory and private placement issuers that is found to be not conclusive is in contrast to other
researchers such as Wruck (1989), Chen et.al (2002), Cronqvist and Nilsson (2005) and
Barclay et.al (2007). However the research of Hertzel and Smith (1993) as well as Lee and
Kocher (2001) report that the evidence of agency cost is not as significant as asymmetric
information. Likewise the conclusion that large firms is the determinants for private
placements issuers is in contrast to the finding of Freeman (1987), Hertzel and Smith (1993),
Lee and Kocher (2001), Wu et. al (2005) and Ferreira and Brooks (2007). Marciukaityte and
Varma (2007) show that most institutional buyers remain passive investors after convertible
debt placements to equity linked securities confirms the monitoring and realignment of
interest by Jensen and Meckling (1976) to reduce the agency cost effect and benefits the
exercises of private placements. Thus the finding of the research does deviate from others
particularly on the influence of agency cost theory and firm size. Nonetheless it is suggested
by the Pearson correlations test that the determinants for private placements issuers are small,
high asymmetric information and limited financial slack that is consistent with the same
researchers’ effect.
Consequently the finding that limited financial slack as the determinants of rights issue
issuers is not supporting the finding of Pilotte (1992), Lee and Kocher (2001) and Brooks and
Graham (2005). The conclusion that higher degree of asymmetric information the
determinants of rights issue issuers is diverging from researchers such as Chemmanur and
Fuighieri (1999) and Lee and Kocher (2001) though supported by Wu et.al (2005). The
finding that suggests the inconclusive result of overvaluation in share price as the
determinants of rights issue issuers is not consistent with Myers and Majluf (1984), Lucas
and McDonald (1990), Lee and Kocher (2001) as well as Lee and Wu (2009). The
proposition that lower agency cost as the determinants of rights issue issuers is not concluded
as such not consistent with researches by Schleifer and Vishny (1986), Hertzel and Smith
(1993), Lee and Kocher (2001) and Wu et al. (2005). The positive relationship between firm
size and rights issue issuers is consistent with the proposition as well as Miller and Rock
(1985) and Lee and Kocher (2001). With exception of firm size effect, the influence of other
factors as the determinants of rights issue issuers in Malaysian market is diverging from
others.
VI. Concluding Remarks
Eventually it is concluded that the determinants for private placements issuers are limited
financial slack, high asymmetric information, undervalued and large. And it is concluded that
the determinants of rights issue issuers are limited financial slack, high asymmetric
information and large. Ultimately the similar characteristic of firms that choose private
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617
placement with firms that choose rights issue is free cash flow, asymmetric information and
firm size while the differentiating characteristic is stock price run-up theory.
The finding that limited financial slack as the determinants of rights issue issuers is contradict
to the past researches though the conclusion of rights issue has reconfirmed the existence of
asymmetric information theory in Malaysia as well as its role in motivating firms to choose
rights issue in the same way to the finding of private placements. Similarly to the previous
finding it is inconclusive that free cash flow effect is related to asymmetric information
theory due to limited empirical evidence. But the conclusion emphasized the domination of
free cash flow effect in the issue of choosing a suitable equity financing methods that is
claimed to be linked with the informational effect. Nonetheless since the influence of free
cash flow to the choice of rights issue is not as proposed it is worth to note that in addition to
informational effect, investment opportunities might be the another initiator.
The finding concludes high asymmetric information as the determinants of rights issue
issuers found in this research is consistent with others even in Singapore. Despite this it is
concluded that both free cash flow effect and asymmetric information theory is crucial in
choosing equity financing method. The conclusion may possibly suggest that the need to
undertake investment opportunities might be the reasons for these firms to ignore specific
preference of equity financing method. Again the similarities of rules and regulation in
Malaysian market and Singapore market in term of no restriction to resell do not lead to the
same finding of asymmetric information theory.
The finding of overvaluation theory is supporting the suggestion that the theory has no
influence over the rights issue issuer that lead to the conclusion that the impact of price runup
theory is not being fully supported in the issue of choosing equity financing method as the
influence is only significant in private placement exercises. Subsequently the finding of
agency cost theory suggested that the theory is not applicable in Malaysian market which
reemphasized the differentiation of finding in Singapore market despite the similar rules and
regulation. The finding is consistent with the suggestion that agency cost theory is not visible
in a market with high ownership concentration level.
Eventually the finding indicates the influence of firm size effect towards the choice of private
placement is not consistent with others inclusive the US market. Nonetheless the partial
relationship of Pearson test between firm size and free cash flow effect as well as firm size
and asymmetric information theory for private placements suggests domination of firm size
effect to explain the determinants of private placements issuers. The partial relationship
suggests that smaller firm does suffer limited financial slack and higher degree of asymmetric
information though further investigation is suggested to confirm the continuation of effect to
the private placement issuers as independent relationship suggested other wise. The partial
relationship between asymmetric information and firm size effect as well as asymmetric
information and price run-up theory for rights issue issuers suggested domination of
asymmetric information theory to explain the determinants of rights issue issuers though
further probing is required as the independent relationship of run-up theory is not
documented.
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Table 1: Number of Corporate Proposals Received by Securities Commission of Malaysia
Source: Bank Negara Malaysia, Monthly Statistical Bulletin, 2009.
Year Private Placement Proposals Rights Issue Proposals
2000 29 45
2001 16 17
2002 38 24
2003 30 44
2004 56 79
2005 38 50
2006 58 21
2007 76 47
Table 3: Multicollinearity Testing
Private Placements Rights Issue
Tolerance VIF Tolerance VIF
FCF .906 1.103 .966 1.035
BVMV .821 1.218 .858 1.166
CAR .989 1.011 .895 1.117
OWNFRAC .965 1.036 .988 1.012
FS .777 1.287 .906 1.104
Table 2: White Heteroskedasticity Testing
F-Statistics Probability
Private Placements 2.458812 0.002713
Rights Issue 2.652745 0.001107
Table 4: Ramsey RESET Testing
F-Statistics Probability
Private Placements 2.673964 0.074582
Rights Issue 1.564560 0.214635
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621
Table 5: Pearson Correlations
LOGGP FCF BVMV CAR OWNFRAC FS
LOGGP 1
{1}
FCF .002
{.038}
1
{1}
BVMV -.082
{.007}
.044
{.020}
1
{1}
CAR -.019
{.047}
-.031
{.010}
-.037
{.314**}
1
{1}
OWNFRAC .021
{-.090}
.165
{.008}
.063
{-.011}
-.045
{-.071}
1
{1}
FS .548**
{.603**}
.228*
{.171}
-.395**
{-.235*}
.080
{-.108}
.052
{-.073}
1
{1}
• The figure in bracket {} is for rights issue.
• ** Correlation is significant at the 0.01 level (2-tailed).
• * Correlation is significant at the 0.05 level (2-tailed).
Table 6: Regression Analysis
Variable Predicted Sign Coefficient
FCF - PPE - -8.67E-07 [4.04E-07] {-2.143287}*
FCF - RI + -6.02E-08 [2.43E-08] {-2.472516}*
BVMV - PPE + 5.20E-08 [1.17E-08] {4.433152}*
BVMV - RI - 1.30E-07 [6.63E-08] {1.966602}**
CAR - PPE - -0.019871 [0.005485] {-3.622509}*
CAR - RI + 1.024836 [0.682643] {1.501277}
OWNFRAC - PPE + -0.003394 [0.165188] {-0.020546}
OWNFRAC - RI - -0.085881 [0.191914] {-0.447495}
FS - PPE - 0.074229 [0.013650] {5.437855}*
FS - RI + 0.304948 [0.045346] {6.724905}*
Weighted Statistics: Cross-section fixed (dummy variables)
Constant 6.188800 F-statistic 14.09445
Adjusted R-squared 0.423595 Prob(F-statistic) 0.000000
• * Significant at 5%
• ** Significant at 10%
• Pooled EGLS method with LOGGP as the dependent variable and cross section weight
• Estimation of CAR is based on Scholes & William’s market model
• The outcome of standard error is stated in []
• The outcome of t-statistic is stated in {}
• The model is corrected with White Heteroskedastic ity-Consistent Standard Errors and Covariance
• The difference in constant value of equation for private placements is 0.273294 and -0.25977 for rights
issue
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622
Effect on Security Prices and Volatility from Cross Listing within the GCC
Markets
Dr. Abraham, Abraham
Department of Finance & Economics
King Fahd University of Petroleum & Minerals
Dhahran, Saudi Arabia
[email protected]
Acknowledgement
The authors are grateful for research support provided by King Fahd University of Petroleum and Minerals.
International Research Journal of Applied Finance ISSN 2229 – 6891
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Abstract
The literature on foreign firms listing their equity in the US is extensive. This paper extends
research in this area by looking at the experience of firms engaging in cross listing across the
regional GCC equity markets. Parametric tests show that on average there is a run up in
prices leading up to the listing date, this however is reversed quickly in the days following
the cross listing. Non parametric results generally support this conclusion. There is also
weak evidence that the variability in returns experience a decline across the event period.
Key Words: Cross-Listing; GCC Markets; Event Study
I. Introduction
Effect on Security Prices and Volatility from Cross Listing within the GCC markets.
The GCC1 (Gulf Cooperation Council States) equity markets have become an important
destination for international portfolio investments by managers seeking opportunities for
diversification. While still lagging behind the developed markets of the US, Europe and
Japan, the local security markets within the GCC have taken important strides in improving
transparency, trading practices and promoting good corporate governance of their listed
companies. Economic integration has arguably been one of the most frequently stated long
term objectives of the GCC states, structured along the lines of the European union and
perhaps one day culminating in a common currency. This paper investigates one dimension
of this integration by looking at the effects of cross listing of stocks within the Council states.
Since the objective of the paper is confined to cross listing within the GCC the analysis does
not consider instances where firms have chosen to issue ADRs (American Depositary
Receipts) in the US or GDRs (Global Depositary Receipts) in London or other international
markets.
Cross listing a stock is not an inexpensive exercise for the firm, there are considerable costs
involved, which include conforming to (in most cases) more stringent disclosure
requirements, the need for greater transparency, being subjected to greater scrutiny by a
larger body of well informed analysts, and the direct administrative, legal and investment
banker costs. What are the counterbalancing benefits that offset these costs? Many sources
of value are identified in the literature, all of these however collapse into one measureable
benefit – the potential to lower the cost of capital for the firm and hence enhance firm value.
Amongst other factors, a lower cost of capital stems from one or more of the following :
International listing may serve to overcome the limitations of segmented national markets
(Doukas and Switzer [2000]), equalizing rates of return for similar risk securities (Errunza
and Miller [2003]), cross listing may be expected to increase liquidity resulting from a larger
base of potential investors, and greater analyst coverage could lead to raised visibility both in
the domestic and international markets, (Baker and Weaver [2002]). Stultz [1999] has also
argued that cross listing could lead to improved corporate governance systems and enhanced
protection for minority holders by managers “bonding” themselves to an extended legal and
regulatory framework. There is a large body of literature on international cross listing,
particularly focused on non US firms choosing to list in the US. The higher degree of
disclosure requirements in the US provides firms the ability to signal their quality to investors
by voluntarily choosing to adhere to the stringent US standards, (Bailey et al [2006]).
Karolyi [1998 & 2006], provides a comprehensive coverage of these and other issues related
to cross listing.
Much less attention has been directed at cross listing within regional markets. This study
specifically looks at the experience of companies within the Gulf that have chosen to cross
1 The Gulf Cooperation Council states consist of Saudi Arabia, Kuwait, UAE, Bahrain, Qatar, and Oman.
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624
list within the GCC region. This regional market with a combined market capitalization of
$680 billion as on December 2009, boasts eight stock exchanges which include Bahrain,
Saudi Arabia, Kuwait, Abu Dhabi, Dubai, Doha and Muscat. As far as we know there have
been no studies that have looked at the experience of cross listing in this important regional
market. Academic literature on the Middle Eastern markets mostly has focused on efficiency
of prices (Abraham et al [2002]), (Omran & Gunduz [2001]). Issues relating to market
integration and dynamic relationship between the GCC stock markets are explored in Darrat
et al [2000] and Hammoudeh & Aleisa [2004]. Bley and Chen [2006] provide a good
overview of the GCC stock markets with historical details of the evolution of these markets.
The decision by a firm to cross list within the GCC markets may seem surprising, given that a
GCC resident in one state is permitted to freely invest in any of the securities listed on the
other exchanges. However there are subtle differences between the different markets when it
comes to international recognition. Dubai and Abu Dhabi are by far the more popular
destinations for international investors by virtue of the concerted efforts made to simplify,
streamline and encourage international participation. Although a smaller market, Bahrain has
stepped up its effort to be an international financial center in the Middle East. Saudi Arabia
on the other hand until recently prohibited any international participation. (Since 2009,
international investors may purchase Saudi stocks through a local custodial arrangement).
All the cross listing that have occurred in the region are therefore either in Dubai, Abu Dhabi,
or Bahrain. For instance the Qatari firm, Qatar Telecom, chose Abu Dhabi to enhance its
international investor base.
The predominant share of cross listing studies address the effect on share prices around the
listing date using a standard event study methodology and have been directed at international
firms listing in the US and vice versa. Two studies deserve special mention in their
comprehensive coverage: Miller [1999] and Foerster & Karolyi [1999]. Both find that there
is a positive abnormal return around the event day, particularly so for firms from the
emerging markets listing in the US. Interestingly enough, the Foerster & Karolyi [1999]
found that the run up of prices up to the event date is followed by average declines during the
post event period.
In this paper we extend these studies by applying the event study methodology to cross
listing of firms within the GCC markets, providing an important empirical contribution to the
international cross listing literature. A number of papers have looked at changes in post cross
listing risk. The evidence seems to suggest that US firms listing abroad generally experience
little or no change in volatility, if anything their beta’s tend to marginally increase. For
foreign firms listing in the US, research shows a significant decrease in local market betas
with little or no change in US market betas, Foerster & Karolyi [1993] and Jayaraman et al
[1993]. In the current study we examine the relative change in local market volatility for
different reference windows using an univariate GARCH representation. This approach is
more likely to capture the time varying nature of volatility resulting from the cross listing
exercise. The paper is organized as follows: Section I describes the data and methodology
used, section II discusses the results, and section III concludes.
II. Data and Methodological Issues.
The sample consisted of 31 companies that cross listed within the GCC states for the period
May 2002 to Jan 2010. Of these, two firms had to be discarded because of insufficient or
inaccurate data on prices that spanned the event window, this left a final sample of 29 firms.
Table I below summarizes the primary listing market and the cross listed destination market.
International Research Journal of Applied Finance ISSN 2229 – 6891
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625
Table I. Distribution of Firms by Primary and Cross listed Markets.
No. of Firms Primary Market Destination Market
6 Kuwait Dubai
14 Kuwait Abu Dhabi
1 Kuwait Bahrain
1 Qatar Abu Dhabi
1 Oman Abu Dhabi
1 Oman Bahrain
3 Bahrain Kuwait
1 Bahrain Dubai
1 Bahrain Abu Dhabi
As can be seen, the majority of firms are from Kuwait wishing to list on the more high profile
Abu Dhabi and Dubai markets. Saudi Arabia is conspicuously missing, as a result of its
domestic policy that prohibits local firms from listing abroad.
There are no reliable data sources to accurately determine the exact date when the cross
listing decision is first mooted and disseminated to the market. We therefore chose to err on
the side of the more reliable date by using the listing date as the event day. The event
window was defined as ±20 days of the event date. Closing prices were collected from -240
days to +120 days of the listing date. The estimation period for the event study is therefore -
240 days to -21 days relative to the event date.
The methodology used in the study follows the standard event study used in the literature.
Abnormal returns are computed for the event window based on parameters estimated for the
market model from the estimation period. Parametric tests in the literature have followed the
developments that have taken place from the seminal works of Ball & Brown [1968], Patell
[1976], and Ball & Brown[1980]. The methodology used in this paper closely mirrors the
Ball & Brown [1980] approach but is modified to account for event induced variance as in
Boehmer, Musumeci, and Poulsen [1991] (BMP). Brief details are provided below for
reference. The notation follows BMP.
Security abnormal returns are defined as: ????,?? ?? ????,?? ?? ?????? ?? ????????,???? with the
notations referring to the standard market index model.
Cumulative average abnormal returns (CAR) are computed by aggregating the
abnormal returns over the relevant time horizon and then averaging over the security
sample..
To compute the test statistic, we first compute each securities’ standardized residual
(SR), where ??^ is the standard deviation of abnormal returns during the estimation
period.
??????,?? ?? ????,?? ????????1 ??
1
??
??
??????,?? ?? ??????????
S ??????,?? ?? ?????????? ??
??????
??
The standardized residuals are then averaged over the security sample and corrected
for event induced variance.
???????? ??
1
???? ??????,??
??
??????
?? 1
?????? ?? 1???? ????????,?? ?? ??
??????,??
??
??
??????
??
??
??
??????
??
Finally the test statistic for the CAR is computed as
International Research Journal of Applied Finance ISSN 2229 – 6891
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626
1
v??
?? ????????
??
??????
Where ?? is the period over which the CAR is computed
The parametric tests described above are clearly dependent on the assumed normality of
returns. Corrado [1989] propose an alternate non parametric rank test to examine abnormal
event returns when departures from normality are of concern. The test begins by ranking
abnormal returns over the estimation and event period and then uses the following test
statistic for a given event date t. The expected rank for firm “i” is given by?? ??
?? ?? ????/2 ??
0.5??, T being the total number of days including the estimation period.
???????????? ??
1??
S ???????? ?? ?????? ?? ??
??????
??^??
Where, ??^?? ?? ????
??
S ??
???? S ???????? ?? ?????????? ??
??????
??
??????
For ?? day multi periods, the rank statistic computed below is unit normal.
???????????? ??
S 1
??
S ???????? ?? ???????? ??
??????
??
??????
??^?? v??
III. Results and Discussion.
Table II provides some summary measures on the average returns and average abnormal
returns for the 29 firms computed over the estimation period. As can be seen, the abnormal
return series departs significantly from normality as measured by the Jarque Bera statistic.
Table II. Summary Measures – Daily returns and Abnormal returns.
Return %
Abnormal
Return %
Mean 0.052 -0.034
Median 0.048 -0.067
Maximum 1.729 4.495
Minimum -1.278 -4.271
Std. Dev. 0.495 0.953
Skewness 0.081 0.200
Kurtosis 3.049 6.928
Jarque-Bera 0.433 233.862
Probability 0.805 0.000
Cumulative abnormal returns shown in table III, computed for various length windows that
span the event date show no significant differences from zero, although in terms of signs the
longer windows show negative abnormal returns while the shorter windows show positive
abnormal returns. One therefore concludes that the cross listing has no effect on returns or
that there may perhaps be non symmetric effects before and after the listing dates that tend to
cancel out.
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627
Table III. Cumulative abnormal returns around listing date
CAR Test Stat
Window
-20 to +20 -2.5373 -1.0139
-10 to +10 -0.2964 -0.3841
-5 to +5 1.3627 1.0838
-2 to +2 0.5603 0.7425
To test for this, the cumulative abnormal returns were disaggregated for pre event vs. post
event windows and are shown in table IV below.
Table IV. Pre event vs. Post event abnormal returns
Pre event CAR Test Stat
-20 to -1 1.4383 0.7843
-10 to -1 1.3696 0.7066
-5 to -1 2.0572 2.0249*
-2 to -1 0.9411 1.3831
Post event
0 to +20 -3.9756 -2.1821*
0 to +10 -1.6660 -1.2044
0 to +5 -0.6944 -0.3810
0 to +2 -0.3808 -0.1708
*Significant at the 5% level
Interestingly, there is a positive run up in values prior to the cross listing date – all the pre
event windows show positive abnormal returns – however a significant positive effect is seen
only within a five day period prior to the event. Much of this run up in prices are however
eroded post event, with all abnormal returns showing negative values after the listing date.
By about 20 days after the cross listing, one sees a significant negative cumulative abnormal
return, negating any gains that accrued prior to the cross listing event.
It is evident from table 1 that the abnormal returns exhibit significant departures from
normality, diluting the results from the parametric tests above. To examine the effects of the
cross listing, we also conduct non parametric rank tests as shown in section 2. Table V
shows the average deviation in ranks (ADR) of abnormal returns and the associated rank test
statistic.
Table V. Abnormal return rank test around the listing date
ADR Test Stat
Window
-20 to +20 -29.9310 -0.3382
-10 to +10 7.0345 0.1111
-5 to +5 59.4828 1.2977
-2 to +2 19.3793 0.6271
The deviation in ranks from the expected rank under the null of no effect cannot be rejected
for any of the intervals, implying that when the pre event and the post event is considered
together, cross listing does not lead to any material effect on security prices. To see whether
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628
the pre event period experience differs from the post event period the test was repeated for
the different sub periods, and is shown in table VI.
Table VI. Pre event vs. Post event abnormal return rank test.
Pre event ADR Test Stat
-20 to -1 51.3793 0.2597
-10 to -1 29.8621 0.6833
-5 to -1 59.2069 1.9159*
-2 to -1 26.2414 0.8492
Post event
0 to +20 -81.3103 -1.2839
0 to +10 -22.8276 -0.4980
0 to +5 0.2759 0.0081
0 to +2 -6.8621 -0.2867
*Significant at the 10% level
Again, none of the intervals show a significant listing effect, except for the immediate 5 day
interval prior to the event date. However the results here are broadly consistent with the
parametric results from table IV, where the abnormal returns are generally positive before the
listing date, with these gains then being dissipated during the post listing period. The
evidence presented here are also in line with those reported in Foerster and Karolyi [1999],
with a 10% pre listing run up of abnormal returns for ADRs in the US, followed by an
average 9% post listing decline.
Finally we also look at changes in volatility with respect to post listing. We have chosen to
use a GARCH representation2 to pick up possible time varying volatility that may be present
due to the cross listing. From table VII it can be seen that although not statistically
significant, volatility generally tends to decline over the event period in magnitude terms.
Table VII. Cross listing induced changes in volatility
Average
Variance*
Variance
Ratio** 5% Critical 10% Critical
Preevent

Event -20 to +20 0.231 1.049 1.539 1.398
Event -10 to +10 0.228 1.065 1.842 1.607
Event -5 to +5 0.232 1.044 2.429 1.987
Post event >+20 0.225 1.079 1.337 1.254
*Average one period ahead forecast variance
**Ratio relative to the pre event average variance
IV. Conclusion.
In this paper we have examined the effect of regional GCC cross listing on equity prices.
Much of the earlier research in this area has focused on foreign firms listing in the US or US
firms listing in overseas markets. The results from this paper therefore extends the cross
listing literature for small emerging regional markets. As argued by Stultz [1999], the
objective to cross list is driven by the desire to lower the cost of capital, which results not
only from the earlier belief of reducing segmented market barriers but also from a variety of
2 We use a low order GARCH(1,1) model: ????
?? ?? ?? ?? ??????????
?? ?? ??????????
??
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
629
other related corporate governance issues. These include improvements in its corporate
governance system, better and more effective protection of minority shareholders stemming
from the required adherence to an extended legal framework, and greater transparency which
lowers monitoring costs.
The event study methodology employed in this paper shows that there is a positive run up in
prices just prior to the cross listing date. However these gains are quickly eroded in the days
following the cross listing. This pattern is consistent with the results reported in the literature
of foreign firms listing in the US. Results are based on both parametric and non parametric
test statistics. The non parametric results, although not statistically significant, is weakly
consistent with the conclusion of the parametric tests. Finally, there is weak evidence that the
variability of returns decreases, going from the pre event period to the post event period.
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Cash Flow-Investment Sensitivity for Manufacturing Firms in America,
Japan and Taiwan
Feng-Li Lin, PhD
Associate Professor
Department of Accounting
Chaoyang University of Technology
Taichung, Taiwan
[email protected]
Jui-Ying, Hung, PhD
Associate Professor
Department of Senior Citizen Service Management
Chaoyang University of Technology
Taichung, Taiwan
[email protected]
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Vol – II Issue – 6 June, 2011
632
Abstract
This essay is to research internal and external cash flow - investment sensitivity for
manufacturing industry in America, Japan and Taiwan in 1997 to 2007. Result of the research
demonstrates that, among the three countries, the manufacturing industry in America has the
highest cash flow-investment sensitivity, and is the only one which invests debt and stocks as
sources of finance into R&D; manufacturing industry in Taiwan has the highest cash flowphysical
investment sensitivity, all of the three countries except America invest cash from
new debts and stocks into physical investment. Only being under the condition of positive
cash flow, will manufacturing firms in Japan invest in R&D and physical asset. Obviously,
among the three countries, America has the largest scale of manufacturing industry and their
only way to keep the leading role is to keep investing in R&D. While most manufacturing
firms in Taiwan are from electronics and heavy industry and they have to focus on physical
investment to enlarge their enterprise scale and develop international market.
Keywords: Cash flow, R&D, Physical investment, Debt issues, Stocks issues
JEL classification: G31, G32
I. Introduction
In history, Fazzari, Hubbard and Peterson (1988) claimed the earliest research about
investment-cash flow sensitivity and found firms with higher financial restraint have
relatively higher cash flow-investment sensitivity. However, Kaplan and Zingales (1997) put
forward different opinions. Their research adopted the Fazzari et al.(1988) sample, but found
firms with lower financial restraint have relativily higher investment-cash flow sensitivity
(Alti,2003; Cleary, 2006; Agca and Mozumdar, 2008). The follow-up researches on
investment-cash flow sensitivity employ various methods to replace financial restraint, in
order to investigate cash flow-investment sensitivity. The latest research was made by Brown
and Petersen (2009), investigating manufacturing firm in America in the period from 1970 to
2006, and found that cash flow-R&D sensitivity was the highest in 1970 to 2006; cash flowgross
investment sensitivity was relatively low but still existed and cash flow-capital
spending sensitivity did not exist.
The fact is, the previous researches merely focused on English-speaking countries, but
seldom on Asian countries. Organization for Economic Cooperation and Development
(OECD) pointed out that, calculated in international exchange rate, GDP of Japan was ranked
only second to America. Moreover, Japan is an insularity, which occupies scarce natural
resources and abundant timber resources, so Japan is not a suitable land for farming and
industrialized at an early age. Taiwan is the fifteenth major economy across the country, with
its GDP ranked the nineteenth all over the world in 2008 and over half of its industries as
service industry and high-tech industry, as well as its international powerful electronic
industry, therefore, this research will discuss the differences among America, Japan and
Taiwan, concerning internal and external (including new debt issues and stocks issues) cash
flow-investment sensitivity, in order to be helpful to investors.
The result of this research reveals that, among the three countries, America has the biggest
scale of manufacturing industry, and the highest cash flow-R&D sensitivity. Manufacturing
firms in Japan will invest in R&D and physical asset only under positive cash flow.
Manufacturing firms in Taiwan has the highest cash flow-physical investment sensitivity.
Obviously, manufacturing firms in America incline to invest internal and external funds into
R&D, while their counterparts in Taiwan incline to invest into physical asset. Thus it can be
seen that, among the three countries, America has the largest scale of manufacturing industry.
The only way to keep the leading role is to keep investing in R&D. While most
manufacturing firms in Taiwan are from electronics and heavy industry, and they have to
focus on physical investment to enlarge their firm scale and develop international market.
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II. Literature Review
II.1 Cash Flow and Physical Investment
The first research about cash flow-investment sensitivity in record was made by Fazzari et
al.(1988), targeting on manufacturing firms in America during the period from 1970 to 1984,
and revealed that the stronger financial restraint was, the higher cash flow-investment
sensitivity was. However, Kaplan and Zingales (1997) claimed different opinions. Their
research adopted the Fazzari et al.(1988) sample, but found firms with lower financial
restraint have relativily higher investment-cash flow sensitivity. Moyen (2004) established
two models to investigate the reason of conflict between Fazzari et al. (1988) and Kaplan and
Zingales (1997). Moyen (2004) found that the research result was in conformity with that of
Fazzari et al. (1988) when judging whether financial restraint existed by the level of payout
ratio, but met the conclusion of Kaplan and¬¬¬¬¬ Zingales (1997) when investigating
respectively by financial restraint models.
Using samples resembling Fazzari et al. (1988), which being adopted data of manufacturing
firms in America from 1969 to 1984, Alti (2003) found that young companies with no
financial restraint and companies with lower payout ratio have higher cash flow-investment
sensitivity. Cleary (2006) targeted on the world’s biggest economies: Australia, Canada,
France, Germany, Japan, the UK and the US, and found companies in larger scale and higher
alloment of shares have relatively higher cash flow-investment sensitivity. Ascioglu, Hegde
and McDermott (2008) studied 1224 firms with over 10 billion capital amount in the year
2000 from S&P 1500 Index, and revealed information asymmetry would cause higher cash
flow-investment sensitivity. Agca and Mozumdar (2008) made a research on manufacturing
firms in America from 1970 to 2001, and got the result that decreasing the imperfection of
capital market could lower the investment-cash flow sensitivity.
While the 7, 176 American companies in the period from 1985 to 2003 studied by
Hovakimian and Hovakimian(2009) showed a lower sensitivity of low cash flow-investment,
and higher sensitivity of high cash flow-investment. Therefore, financial restraint will
influence the investment strategy of a company and its cash flow-investment sensitivity, but
in order to enlarge firm scale and enforce competitive power, a company has to increase
physical investment when capital is affluent. On the contrary, when running short of cash,
investment will tend to be conservative and physical investment will be reduced.
II.2 Cash Flow and R&D
In the past, many scholars took the opinion that R&D could upgrade firm value. Szewczyk,
Tsetsekos and Zantout (1996) researched the American listed companies from 1979 to 1992,
and found a positive correlation between R&D and abnormal returns. Tubbs (2007) limited
the research in the period from 1974 to 2001 and demonstrated that increasing R&D would
lead to the improvement of production capability and service, and therefore inrease sales.
Furthermore, significant abnormal returns will pay to companies that keep adding R&D for
continuous five years. A study by Pyykkö (2009) showed that, in Europe, the acquired
companies will increase their stock values via R&D.
Accordingly, this study takes the view that investment in R&D can not only increase
company value, but also strengthen competitive advantages. Companies will actively invest
in R&D when having relatively affluent funds; in contrast, companies will reduce
expenditure on R&D when finance is tight. Nonetheless, Brown and Petersen (2009) claimed,
total investment consists of R&D and physical investment is the best combination for
investment. Consequently, this research divides cash flow into positive cash flow and
negative cash flow, while the former represents conditions with affluent cash, and the latter
represents conditions with tight finance. And this research analyses sensitivity of cash flow-
R&D, and sensitivity of cash flow- physical investment respectively.
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II. 3 External Fund and Investment
Lyandres (2007) investigated American Companies in the period of 1951 to 2005, and got a
conclusion that cost of external funds influenced investment-cash flow sensitivity. Ogawa
(2007) studied the influence of new debt issues on R&D among Japanese manufacturing
firms in the 1990’s, and found a negative correlation between new debt issues and R&D.
Ovtchinnikov and McConnell (2009) claimed that sensitivity of stock value and investment
will be influenced by debt and tight finance. In addition, a healthy capital market can improve
capital and help companies to gather funds of positive net present value. Baum, Caglayan and
Talavera (2010) researched on American manufacturing companies in the period of 1988 to
2005 and found that debt would be produced because of the uncertain change in market, and
therefore influence capital spending. All of the above researchers found that new debt issues
and stock issues have influences on investment. Taking American manufacturing companies
during the period of 1979 to 1996 as samples, Bhagat, Moyen and Inchul (2005) investigated
their financial conditions under financial difficulties, and found poor managed companies
would try to survive by external funds gathered through speculation on stocks. Whereas, this
research deems that companies will probably use external funds to fill up financing gap under
negative cash flow, and thus leads to relatively lower sensitivity; while companies will
probably use external funds for investment under positive cash flow, and therefore leads to a
relatively higher sensitivity.
III. Methodology
III.1. Sample Selection
This research takes manufacturing firms in America, Japan and Taiwan (SIC codes 2000-
3999) as samples, and the sample period lasts from 1997 to 2007. The material resources are
Compustat and Compustat_Global. The following materials are deleted from this research:
manufacturing firms not listed; manufacturing firms miss materials about R&D and capital
spending for over three years; abnormal values when R&D and capital spending are negative;
and outliers as 1% of regression value.
As a result, this research employs 3196 American manufacturing firms in total, deletes 1530
firms with missing materials for over three years, 8 firms with abnormal values and 32 firms
with outliers, finally adopts 1626 firms as samples. And 1687 Japanese manufacturing firms
in total are gathered, among which 458 firms with missing materials for over three years, 4
firms with abnormal values and 17 firms with outliers are deleted, and therefore 1216 firms
are taken as samples. Moreover, 929 Taiwanese manufacturing firms are collected, among
which 832 firms with missing materials for over three years and 9 firms with outliers are
deleted, and consequently adopted 88 firms as samples. The sample forms are in time series,
and the sample selection process is demonstrated in Table 1.
III.2 Model Design and Variable Measurement
Adopting ordinary least square method (OLS), this research firstly detects cash flow-physical
investment sensitivity, cash flow- R&D sensitivity for manufacturing firms in America, Japan
and Taiwan. Then, this essay analyses, external funds (debts and stocks)- physical investment
sensitivity and external funds-R&D sensitivity. To control differences of firm sizes, all
variables are scaled by beginning of-period total assets (TA). This essay employs the method
of La Porta, Lopez-de-Silanes and Vishny (2002) to calculate Tobin’s Q. The higher the Q
value is, the greater chance for further development, the higher firm value is. Table 2 is the
definition and scale for model variable. All materials adopt time series and build up the
following models:
(CAP/TA)it=ß1(CAP/TA)it-1 + ß2(NCF/TA) i,t +ß3(Q)it-1 +ß4(DBT/TA)it
+ß5(STK/TA)it+dt+ai+?i,t (1)
(CAP/TA)it=ß1(CAP/TA)it-1 + ß2(PCF/TA) i,t +ß3(Q)it-1 +ß4(DBT/TA)it
+ß5(STK/TA)it+dt+ai+?i,t (2)
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(R&D /TA)it=ß1(R&D/TA)it-1 + ß2(PCF/TA) i,t +ß3(Q)it-1 +ß4(DBT/TA)it +
ß5(STK/TA)it+dt +ai+?i,t (3)
(R&D /TA)it=ß1(R&D/TA)it-1 + ß2(PCF/TA) i,t +ß3(Q)it-1 +ß4(DBT/TA)it +
ß5(STK/TA)it+dt +ai+?i,t (4)
IV. Results
Table 3 demonstrates 17,886 narrative statistics of variables concerning the American
manufacturing firms, 13,376 concerning the Japanese manufacturing firms, and 968
concerning the Taiwanese manufacturing firms during the period from 1997 to 2007. The
mean value of total assets (Assets) of manufacturing firms in America, Japan and Taiwan are
4091.108 billion, 1627.957 billion and 1.4907 billion respectively. It shows that the
American manufacturing industry is of the largest scale, and Japanese is the second. The
mean value of R&D to total assets in American, Japanese and Taiwanese manufacturing
firms are 0.178197, 0.022881 and 0.019671 respectively, which shows that the American
manufacturing industry invests the most in R&D, while the Japanese is the second, and
Taiwanese is the least. The mean value of physical investment (CAP) to total assets in
American, Japanese and Taiwanese manufacturing firms respectively are 0.044733, 0.037873
and 0.061066. We can see that the Taiwanese manufacturing industry has the highest
physical investment, being followed by the American, while the Japanese has the lowest.
The mean value of the ratio of gross cash flow (GCF) to total assets in American Japanese
and Taiwanese manufacturing firms respectively are -0.13301, 0.074018 and 0.053874. It
shows that the American manufacturing industry is under negative cash flow, while the
Japanese and Taiwanese are under positive cash flow. The mean value of ratio of DBT to
total assets in American, Japanese and Taiwanese manufacturing firms respectively are
0.03088, -0.00395 and 0.11195, which means that the debenture issued by the American
manufacturing firms are higher than that by the Taiwanese, while the Japanese are recorded
with a higher number of indebtedness. The mean value of ratio of STK to total assets in
American, Japanese and Taiwanese manufacturing firms respectively are 0.116957, 0.001501
and 0.014062. It demonstrates the STK of the American manufacturing industry is higher
than that of Taiwanese and Japanese.
After dividing cash flow further into negative cash flow (NCF) and positive cash flow (PCF),
the mean value of negative cash flow in American, Japanese and Taiwanese manufacturing
firms respectively are -0.91513, -0.06015 and -0.43613, which shows that the American
manufacturing industry is under the highest negative cash flow, while the Taiwanese is the
second. The mean value of positive cash flow (PCF) in American, Japanese and Taiwanese
manufacturing firms respectively are 0.16261, 0.082001 and 0.10488, which shows that the
American manufacturing industry has the highest positive cash flow, being followed by the
Taiwanese.
Panel A in Table 4 shows that, under gross cash flow, a negative correlation (t value for -
8.366733) exists between gross cash flow and physical investment in American
manufacturing firms, with coefficient for -0.001858, while a positive correlation (t value for
7.757040) exists in Japanese manufacturing firms, with coefficient for 0.043785, and the
factors in Taiwanese manufacturing firms are insignificant. Concerning the controlled
variable, only Q value for Taiwanese manufacturing firms is in negative correlation (t value
for -4.914265); all three countries display significant positive correlation in DBT and STK,
which represents that under gross cash flow, all the countries will invest external funds
combined of debts and newly issued stocks into physical investment. Panel B of Table 4
shows that, negative correlation (t values respectively are -5.235001 and -1.73752) exists
between negative cash flow and physical investment in both America and Taiwan, with
coefficients is -0.001577 and -0.002427 respectively, while the data in Japan are
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insignificant. When displaying negative cash flow, Taiwanese manufacturing firms will
deduct physical investment more than American, and positive correlation exists between
debts and newly issued stocks, and physical investment. Under negative cash flow, external
funds combined of debts and newly issued stocks will be invested into physical investment.
Panel C in Table 4 shows that, with coefficients respectively are 0.126623 and 0.166171,
positive correlation (t values respectively are 20.06789 and 5.931173) exists between
positive cash flow and physical investment in Japanese and Taiwanese manufacturing firms,
in which Japanese demonstrates a higher sensitivity than American, while the factors for
American are insignificant. When displaying positive cash flow, except for newly issued
stocks and physical investment in American manufacturing firms, the rest display positive
correlation, which mean, under positive cash flow, except that America will not invest cash
from new stocks into physical investment, all of the three countries will invest external funds
combined of debts and new stocks into physical investment. This result conforms to opinions
claimed by Brown and Petersen (2009).
Panel A in Table 5 shows that, positive correlation (t value for 5.964748) exists between
gross cash flow and R&D in Japanese manufacturing firms, while negative correlation (t
value for -2.209228) exists in Taiwanese manufacturing firms, and the factors in American
manufacturing firms are insignificant. It means that Japanese manufacturing firms will invest
the most gross cash flow into R&D, while Taiwanese manufacturing firms will reduce R&D.
Panel B in Table 5 shows that, negative correlation (t value for -5.035535) exists between
negative cash flow and R&D in Japanese manufacturing firms, with coefficient of -0.017184,
while that factor in American and Taiwanese manufacturing firms is insignificant. It means
that when displaying negative cash flow, Japanese manufacturing firms will tighten R&D.
Panel C in Table 5 shows that, positive correlation (t values respectively are 15.94211,
14.79481 and 3.979810) exists between positive cash flow and R&D in manufacturing firms
from America, Japan and Taiwan, with coefficients respectively are 0.155853, 0.027863 and
0.035304. It reveals that the investment of positive cash flow into R&D by American
manufacturing firms is the highest among the three countries. Regarding the controlled
variables in Panel A, B and C, Q is in significant positive correlation (t value for 2.006135)
only in Taiwanese manufacturing firms, while Q is insignificant in America and Japan.
Considering controlled variables of external debts and newly issued stocks, positive
correlation exists between debts and R&D, as well as newly issued stocks and R&D in
American manufacturing firms. The phenomenon shows that the American manufacturing
firms will invest funds gathered from both debts and newly issued stocks into R&D, under
conditions of gross, positive or negative cash flow. Except for insignificant negative cash
flow, Japanese manufacturing firms display a negative correlation exists between debts and
R&D, as well as newly issued stocks and R&D, which reveals that Japanese manufacturing
firms will not invest funds, gathered from debts and newly issued stocks into R&D. Only
under gross cash flow and positive cash flow, will Taiwanese manufacturing firms display a
negative correlation between newly issued stocks and R&D. It means that Taiwanese
manufacturing firms will not invest funds gathered from newly issued stocks into R&D.
Therefore, among the three countries, America is the only country which invests funds from
debts and newly issued stocks into R&D.
The adjusted R2 in Table 4 and 5 fluctuates between 38%~92%, which displays the excellent
interpretability of this study model. Through Table 4 and 5, it shows that under positive cash
flow, investment into R&D from the American manufacturing firms is the highest among the
three countries. However, America is also the country which makes the highest deduction in
R&D when under negative cash flow. Manufacturing firms in Japan will invest in R&D, as
well as physical investment, only under the condition of positive cash flow. While
manufacturing firms from Taiwan invest the most into physical investment when having
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637
positive cash flow, and execute the highest deduction in this part when having negative cash
flow. Considering external funds, except that America will not invest cash from new stocks
into physical investment, all of the three countries will invest cash from debts and new stocks
into physical investment. While among the three countries, America is the only country
which invest funds gathered from debts and new stocks into R&D.
Conclusion
This research investigates internal and external cash flow-investment sensitivity for
manufacturing firms in America, Japan and Taiwan, focusing on the period from 1997 to
2007, and analyses differences in three countries. The results demonstrate that, under positive
cash flow, investment into R&D from the American manufacturing firms is the highest
among the three countries. However, America is also the country makes the highest
deduction in R&D when under negative cash flow. Manufacturing firms in Japan will invest
in R&D, as well as physical investment, only under the condition of positive cash flow.
While manufacturing firms from Taiwan invest the most into physical investment when
having positive cash flow, and execute the highest deduction in this part when having
negative cash flow. Considering external funds, except that America will not invest cash from
new stocks into physical investment, all of the three countries will invest cash from debts and
new stocks into physical investment. Among the three countries, America is the only country
will invest funds gathered from debts and new stocks into R&D.
Obviously, manufacturing firms in America value R&D, while manufacturing firms in
Taiwan treasure physical investment. The reason for American manufacturing firms value
R&D lies on the ground that creativity produced by R&D can not only strengthen production
capability, improve product quality and lower cost, but the intellectual property produced can
also harvest option premium. Furthermore, it is common to apply for loans in America, where
R&D fund for investment loans established by the state are available in lower-than-market
interest for qualified companies. Manufacturing firms in Taiwan value physical investment
for the reason that physical investment can obtain lower loan interest, while investing into
R&D, firms have to get guarantee fund before applying for credit guarantee fund, whose
interest is appreciably higher than physical investment. Therefore, manufacturing firms in
Taiwan incline to invest cash flow into physical investment. Obviously, among the three
countries, America has the largest scale of manufacturing industry. The only way to keep the
leading role is to keep investing in R&D. While most manufacturing firms in Taiwan are
from electronics and heavy industry, and they have to focus on physical investment to enlarge
their firm scale and develop international market.
References
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investment-cash flow sensitivity. Journal of Banking & Finance, 32(2), 207-216.
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under uncertainty. Economics Letters, 106(1), 25-27.
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declined so sharply? Rising R&D and equity market developments. Journal of Banking
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Table 1 Sample Selection Process
American manufacturing firms observations
manufacturing firms in America over 1997-2007 (SIC codes 2000-3999) 3196
missing R&D, capital spending and total assets for over three years (1530)
abnormal values (R&D and capital spending observations are negative) (8)
outliers (32)
total 1626
Japanese manufacturing firms
manufacturing firms in Japan over 1997-2007 (SIC codes 2000-3999) 1687
missing R&D, capital spending and total assets for over three years (458)
abnormal values (R&D and capital spending observations are negative) (4)
outliers (17)
Total 1216
Taiwanese manufacturing firms
manufacturing firms in Taiwan over 1997-2007 (SIC codes 2000-3999) 929
missing R&D, capital spending and total assets for over three years (832)
outliers (9)
total 88
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639
Table 2 Variable Measurement
Table 3 Summary descriptive for manufacturing firms in America, Japan, and Taiwan
Variable America Japan Taiwan
Assetst Mean
Median
4091.108
129.31
1627.957
444.25
1.490777
0.592697
(R&D/TA),t Mean
Median
0.178197
0.066176
0.022881
0.016328
0.019671
0.013139
(CAP/TA)t Mean
Median
0.044733
0.031103
0.037873
0.03041
0.061066
0.041903
(GCF/TA)t Mean
Median
-0.13301
0.101495
0.074018
0.072622
0.053874
0.085858
Qt-1 Mean
Median
4.395917
1.909248
6.299945
4.428669
0.436231
0.470992
(DBT/TA)t Mean
Median
0.03088
0
-0.00395
-0.00077
0.11195
0.087271
Code Name Measurement
CAP/TA Capital spending Compustat data 46
R&D Research & development Compustat data 128
PCF/TA Positive cash flow after-tax income before extraordinary items plus R&D and depreciation
NCF/TA Negative cash flow after-tax income before extraordinary items plus R&D and depreciation
Q Tobin’s Q
(book value of total assets-book value of equity -deferred inco
tax+market value of common stock)/book value of total assets
DBT/TA Net new long-term debt Issuance of long-term debt – buyback of treasury stocks
STK/TA Net new stock issues Issuance of preferred stocks and common stocks – Treasury stocks
dt year fixed effects Control for year fixed effects
ai Specific effect
a firm specific effect thatcontrol for all time-invariant determinates of R&D
the firm level
?i,t Error term a random error term
TA Total assets Compustat data 6
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(STK/TA)t Mean
Median
0.116957
0.002214
0.001501
0
0.014062
0
NCF Mean
Median
-0.91513
-0.20422
-0.06015
-0.03096
-0.43613
-0.03189
PCF Mean
Median
0.16261
0.13812
0.082001
0.075857
0.10488
0.094176
Observations 17886 13376 968
Table 4 OLS estimates of the cash flow - physical investment Sensitivity
America t-value Japan t-value Taiwan t-value
Panel A:all firms
Qt-1 2.44E-06 0.125191 6.63E-06 1.368257 0.078748 -4.914265***
(GCF/TA)t -0.001858 -8.366733*** 0.043785 7.757040*** -0.001711 -1.227828
(DBT/TA)t 0.007567 7.010897*** 0.061069 11.52882*** 0.078748 4.615799***
(STK/TA)t 0.002916 2.859127*** 0.045414 4.784612*** 0.168871 4.975452***
Observations 11811 8484 874
Adj.R2 0.362289 0.567094 0.354161
Panel B:NCF firms
Qt-1 -6.93E-05 -2.692639*** 0.000721 4.428360*** -0.056615 -1.643280
(NCF/TA)t -0.001577 -5.235001*** 0.004260 0.483280 -0.002427 -1.737528*
DBT/TA 0.006193 4.024205** -0.014135 -1.097268 0.107894 1.782857*
(STK/TA)t 0.002508 1.863616* -0.007403 -0.655533 0.159538 1.861050*
Observations 3099 478 84
Adj.R2 0.148439 0.304921 0.307371
Panel C:PCF firms
Qt-1 0.000457 4.283602*** 1.92E-06 0.370554 -0.029688 -3.121541***
(PCF/TA)t 0.003177 1.597061 0.126623 20.06789*** 0.166171 5.931173***
DBT/TA 0.008118 2.997290*** 0.073092 11.99804*** 0.103226 5.737900***
(STK/TA)t -0.012606 -4.566889*** 0.067879 5.768129*** 0.178232 4.919816***
Observations 8712 7851 790
Adj.R2 0.396024 0.459454 0.375828
*, **, ***Significant at the 10%, 5%, and 1% levels, respectively.
Table 5 OLS estimates of the cash flow - R&D sensitivity
America t-value Japan t-value Taiwan t-value
Panel A:all firms
Qt-1 0.000176 0.914191 1.21E-06 0.857779 0.007664 2.006135***
(GCF/TA)t 0.002214 1.027818 0.007651 5.964748*** -0.018869 -2.209228***
(DBT/TA)t 0.277085 26.17764*** -0.006428 -4.300176*** -0.020483 -2.705033***
(STK/TA)t 0.207560 20.65692*** -0.015202 -6.683147*** -0.019786 -2.020476***
Observations 11873 8979 569
Adj.R2 0.437092 0.912005 0.751916
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Panel B:NCF firms
Qt-1 0.000368 1.144061 3.57E-05 0.558234 0.007652 0.241240
(NCF/TA)t 0.004701 1.288119 -0.017184 -5.035535*** -0.036052 -0.643627
(DBT/TA)t 0.299610 15.52137*** 0.004485 0.905057 0.026273 0.653669
(STK/TA)t 0.255435 15.20639*** -0.023033 -5.184902*** -0.034173 -0.665737
Observations 3106 508 32
Adj.R2 0.329422 0.837368 0.565456
Panel C:PCF firms
Qt-1 -0.000329 -0.716966 2.49E-07 0.179876 0.005079 1.589690
(PCF/TA)t 0.155853 15.94211*** 0.027863 14.79481*** 0.035304 3.979810***
(DBT/TA)t 0.414456 35.50291*** -0.007177 -4.530682*** -0.006321 -1.140086
(STK/TA)t 0.244284 19.39484*** -0.022661 -7.637182*** -0.050489 -4.474260***
Observations 8767 8310 537
Adj.R2 0.379344 0.916072 0.644028
*, **, ***Significant at the 10%, 5%, and 1% levels, respectively
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Determinants of the ‘Decision to Finance’ in Micro Finance Institutions
Prof. Fedhila Hassouna, PhD
Professor of Accounting
Mannouna University
Tunis, Tunisia
Dr. Mehdi Mejdoub
Assistant Professor
Ecole Superieure de Commerce
Mannouba University
Tunisia
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Abstract
The objective of this paper is to identify the factors that explain credit rationing by microfinance
institutions. The results of a Logit model test on our emerging market Tunisian data
indicate that the absence of a “Guarantor” constitutes the main obstacle to access to credit. In
rebuilding the economy of Tunisia it is clear that long-term relationships will increase
applicant chances of access, while the ‘sector of activity’ hasn’t (historically) had any impact
on the micro finance decision. Finally, we find no evidence of discrimination against the poor
- while women benefit from positive discrimination.
KEY-WORDS: micro-finance, credit rationing, micro-finance long-term relationship.
I. Introduction
Micro-finance is lauded as a new instrument to reach and facilitate micro-entrepreneurs who
do not have access to traditional financial products, given the hype it is remarkable that the
volume of financial intermediation has not met expectations [Claessens: 2006; Christen et al.
2004; Honohan: 2004]. The exclusion of the majority of micro-entrepreneurs from the
services of Micro-Finance Institutions (MFI) is due, not only to self-exclusion, but also to
problems of credit’s rationing by the MFI [Claessens: 2006; Honohan: 2005; Kempson et al. :
2000; Morduch: 1999a; and Baydas et al. : 1994]. This rationing stems in part from the
unbalance between the offer and the demand of credit and in part from the inability of the
mechanism of price to balance the market of credit [Stiglitz and Weiss: 1981].
In Tunisia, Mejdoub and Mamoghli (2009) estimated that the rate of penetration of the MFI,
that have the status of "local development associations"3, would not exceed 25% of eligible
micro-entrepreneurs. This rate is very worrying considering the effort conducted by the new
government to reach the maximum population in the remote areas. This historically low rate
could be explained by the rationing of credit. In fact, Coleman (2006), Honohan (2005),
Morduch (2000), Mosley (2001), Amin et al. (1999), Evans et al. (1999) and Buvinic et
Berger (1990) all show that eligible micro-entrepreneurs are suffering from loan rationing by
MFIs. Therefore, the objective of this research is to investigate the factors underpinning the
rationing of credit pursued by the MFIs – and where possible contextualize this in the
Tunisian emerging market. Basically this research will seek to identify the specific factors
that are used to justify rationing of credit to the micro entrepreneur.
The sample on which this work is carried out is composed of 146 micro-entrepreneurs. Data
are collected by questionnaire during the months of June and July 2007. A Logit model is
specified in order to explain the reasons of rationing of the credit by local development
associations. Results show that: first, the absence of guarantor constitutes the main obstacle
to the access to financing. Second, the duration of the customer relationship with the FMI
decreases the likelihood of credit rationing. Third, the micro-entrepreneur's reputation in
his/her community is a criterion of selection; however when he/she cannot be guaranteed by a
guarantor, such reputation does not have any effect on the financing decision. Fourth, females
have a higher likelihood of obtaining financing from the MFI than men. Fifth, agriculture
activity, as privileged by the associations, doesn’t improve the likelihood to access to credit
granted by the MFI. Finally, entrepreneurship profile does not improve the likelihood to
obtain MFI financing, however when such criteria is supplemented by providing a guarantor,
the likelihood of rationing micro-credit, will be decreased.
Our findings suggest that the guarantor constitutes the major criteria that explain credit
rationing. Such findings, makes our research relevant – particularly compared to others which
3 In Tunisia, the MFI are called "local development associations" having the non-profit association status, and
operating in all delegations, to serve micro-entrepreneurs in financial services. They are exclusively financed by a
state bank, the Tunisian Bank of Solidarity (BTS).
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do not consider such determinants in explaining the likelihood of rationing credit. In fact this
finding is of direct relevance to Tunisian reconstruction policymakers seeking to broaden the
support for MFI reach and implementation. The rest of this paper is organized as follows.
First, the literature review is discussed and hypotheses are developed (along with robustness
tests); second, the research methods are explained; then empirical results are reported and
discussed; finally this paper ends with a summary and conclusions.
II. Literature Review and Hypotheses Development
Some previous works identify certain number of factors that explain financial exclusion, and
particularly the rationing of the credit as made by the MFI. These factors are: (1) reputation,
(2) entrepreneurial profile, (3) poverty, (4) customer relationship with the IMF, (5) sector of
activity (6) guarantee and collateral, (7) gender. Next we will review the major researches
that explained how these factors affected the rationing policy of the FMI and subsequently,
we will state our hypothesis.
According to Diamond (1989), the reputation of the borrower has an impact on his/her access
to financing. Berger et al. (2001) explain that the reputation allows the bank to collect private
information about the entrepreneur, which will be used at the time of the loan screening. In
addition, Howorth and Moro (2006) report that in small cities, banks do not finance the
entrepreneur who has a bad reputation in his/her community. Therefore, reputation is
considered as a major factor in loan decision making. In the area of micro-finance, Wenner
(1995), Aghion and Morduch (2000), Casson and Giusta (2004) and Coleman (2006) showed
that the better reputation a micro-entrepreneur benefits in his/her community, the luckier
he/she is to get the financing from the MFI. Thus we predict that:
Hypothesis 1: The reputation of the micro-entrepreneur has a negative effect on the
likelihood of loan rationing.
Since Schumpeter (1934), it seems that the entrepreneurial profile has an effect on the
financing decision of the bank. Some more present studies confirm these first intuitions in the
field of the micro-finance. Morduch and Haley (2002) explain that services of the MFI should
be destined only to people having an entrepreneurial profile, and, therefore, able to insure the
success of a micro-enterprise. Khandker (1998) demonstrates that the distribution of financial
services to people who do not have entrepreneurial skills increases the risk of defaulting.
These findings suggest the following hypothesis:
Hypothesis 2: The entrepreneurial profile has a negative effect on the likelihood of loan
rationing.
The effect of the poverty on the financing decision of the MFI nourishes a lot of debates in
the literature on micro-finance. In fact, it is through its ability to touch the poor that the
micro-finance emerged, developed and is revealed as a new financial intermediation [Barr,
2005,; Seck, 2007,; Morduch, 1999b,; Moll, 2005,; Morning et al., 2002]. However, most
researches showed that loans granted to poor entrepreneur represent low percentage in MFI
loan portfolio. [Najavas et al. 2000; Morduch: 1998; Evans et al. 1999; Datta, 2004]. Such
inadequacy suggests the following hypothesis:
Hypothesis 3: Poverty has a positive effect on the likelihood of loan rationing.
Such relation could be moderated by the following two factors: (1) Size of the MFI, (2)
entrepreneur age and education level. Cull et al. (2007) and Hartarska (2005) conclude that
the size of the institution has an effect on the relation between poverty and credit rationing.
On the other hand, Datta (2004) and Cleassens (2006) respectively explain that entrepreneur
age and education level can moderate the relation between poverty and credit rationing. This
research will consider these two factors as moderate variables.
Sustainable relation with the financial institution is considered to be a positive factor in the
process of screening and granting financial facilities. Such relation has been validated by
Petersan and Rajan, (1994), Angelini, Salvo and Ferri (1998), Elsas and Krahnen (1998),
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Lehmann and Neuberger (2001), Bodt, Lobez and Statnik (2005). In micro-finance, several
researches demonstrate the importance of such sustainable relationship on the credit decision
made by the MFI. . Pollinger et al. (2007) suggest that the duration of customer relationship
contributes to reduce the cost of credit monitoring, which is high in micro-finance, by
attenuating the information asymmetry cost. According to Honlonkou et al. (2006) and Lanha
(2002), MFI becomes less demanding in terms of guarantees and of follow-up with microentrepreneurs
who build up a sustainable relationship and demonstrate success in their
previous endeavors. These contentions are captured in the following hypothesis:
Hypothesis 4: customer relationship with the MFI has a negative effect on the likelihood
of loan rationing.
The business activity is a major component in assessing credit risk. The agricultural
enterprises are those that suffer most from credit rationing [Beck et al.: 2004; Beck and De
La Torre.: 2006]. They are considered to be very risky due to the following phenomena: (1)
climatic conditions [Zeller and Sharma: 2000], and market volatility [Beck and De La Torre.:
2006]. Hence, such risk is idiosyncratic and precludes financial institutions from financing
such activities [Beck and De La Torre: 2006]. Schreiner: 2003 demonstrates that microagriculture
businesses are much more risky than other type of activities such as confection or
service industries. Honlonkou et al. 2006 finds that the risk of non-repayment is higher for
micro-entrepreneurs running agricultural activities. This inherent risk could increase the
likelihood of excluding micro-agriculture from accessing MFI financing [Baydas et al.:1994].
Accordingly, we propose that:
Hypothesis5: The agricultural activity, that the micro-entrepreneur tries to finance, has
a positive effect on the likelihood of loan rationing.
In banking intermediation collaterals and corporal guaranties are considered to be one of the
major components of any credit decision. Without such support, the banks are reluctant to
provide any financing facilities. This condition explains largely the exclusion of microentrepreneurs
from getting any financial supports from traditional commercial banks [Pretes:
2002; Snow & Buss: 2001, Morduch: 1999a].
In an effort to grant credit facilities to micro-entrepreneurs who are powerless to offer any
corporal guaranties, a new financial intermediation emerged with the essence to offering
financing without requiring corporal guarantees. The first MFIs substitute corporal
guarantees by the system of joint responsibility, on which the technique of group credit4 is
based. [Ghatak: 1999; Ghatak and Guinane: 1999; Morduch: 1999a; Ghatek: 2000].
However, such system was not sufficient in remote area where people do not trust each other.
Although these MFIs are emerged to help micro-entrepreneur in the process of financing their
small projects, Honlonkou et al. (2006) indicated that in Benin, some MFIs still require
corporal guaranties such us land or agricultural material, in counter part of access to credit.
Aghion and Morduch (2005) identified the same credit conditions required by IMFs in
Albania. Although there are few researches works on the effect of guarantees on the rationing
of the credit, it would be advisable to anticipate a negative relationship between the two
variables. Hence, we hypothesize that:
Hypothesis 6: guarantors and collaterals have a negative effect on the likelihood of
loan rationing.
The micro-entrepreneur's gender can have an impact on the financing decision made by the
MFIs. Morduch (1999a) and Mosley (2001) showed that women constitute a favored clientele
4 The technique of group credits foresees that the bank first starts with constituting groups of micro-entrepreneurs
(3 to 7 members) within the community that is in its field of activity. It proceeds thereafter to the financial services
distribution to some of these micro-entrepreneurs. If these refund, then it continues to finance the rest of the group.
If not, the other members are obliged to refund in their favor to the risk to be excluded of financing [Aghion and
Morduch: 2000].
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of the MFI and represent respectively 94% and 60% of customers’ portfolio of the Gramenn
Bank and the Banco-Sol, which are considered to be the biggest MFI in the world. Women
are considered by the MFIs as more serious, honest and creditworthy than men (Brau and
Woller, 2004; Pitt and Khandker, 1998; Morduch, 1999a; Schreiner, 2003). However,
Buvinic and Berger (1990) and Evans et al. (1999) respectively showed that in Peru and
Bangladesh, micro-entrepreneur women are more rationed by the MFIs than their
counterparts, men. Considering these inconsistencies, it is appropriate to formulate the
following hypothesis:
Hypothesis 7: The gender has an effect on the likelihood of loan rationing.
According to Buvinic and Berger (1990) and Berger (1989), levels of training and education
of women can influence their access to financing. Therefore, these factors will be retained as
moderate variables of the relationship between gender and loan rationing.
III. Method
This section presents the methodology carried out by the researchers. First we will present the
sample selection method. Second we will justify our variables’ measurement instrument.
Third, the data collection method is specified and finally we will specify the likelihood
model.
Sample Selection
The sample is composed of 146 micro-entrepreneurs being in the area of Siliana (located in
the north-west of Tunisia). This location is selected by the researchers for two reasons: first,
the area is considered by the Tunisian government to be remote and requires full support to
develop small projects carried on by local entrepreneurs. Second, we were able to identify
four MFIs operating in the area for the last four years. This timeframe is considered by the
researchers to be sufficient enough to build up a data basis for analysis. Among the four
associations, only three accepted to participate in this project; the other one declined by
showing no interest in the project. To build up the sample, researchers accessed to the
micro-credit applications register of each of the three associations. They classified the loan
requests, as recorded in the application registers, between accepted for financing and
rationing. Our sample was selected randomly from each group from each association. The
financed people and those rationed by the credit represent respectively 71% and 29% of the
sample. The middle age of micro-entrepreneurs is 41 years and women represent more than
37% of the sample. Nearly half of micro-entrepreneurs do not have a level of education and
the majority has not attended a technical training in advance. People who want to finance
some agricultural activities constitute nearly 44% of the sample. Table 1 describes the
composition of the retained sample.
Table 1 : Description of the sample
FREQUENCY PERCENTAGE
EXCLUSION
Refused 42 28.8
Financed 104 71.2
GENDER
Male 92 63.0
Female 54 37.0
LEVEL OF EDUCATION
Educated 78 53.4
Non educated 68 46.6
LEVEL OF TRAINING
Trained 39 73.3
Non trained 107 26.7
.SECTOR OF ACTIVITY
Agricultural 64 43.8
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Non agricultural 82 56.2
DÉLÉGATIONS
Bergou 51 34.9
Rouhia 44 30.2
Southern Siliana 51 34.9
Size of the sample = 146 people
Measurement of Variables
Credit rationing
Stiglitz and Weiss (1981) define the rationing of the credit as of situations where: "(a) among
applicants for credits who seem to be similar, some receive a credit whereas others not, and
applicants who are rejected will not receive a credit even though they are ready to pay for a
high interest rate; or (b) there are groups of individuals among the population who, for a
given offer of credit, are enable to get a credit, whatever the interest rate is, and who will be
able to get it for a larger offer of credit". Therefore, this variable will be binary: zero value if
the loan request is financed and the value 1 if it is rejected.
Poverty
Reference to Evans et al. (1999) and Datta (2004) methodology, this research used annual
expenses by unit of consumption to measure this concept. The expenses quartiles are used as
criteria for scattering the sample into different socio-economic groups. Considering this
instrument, our sample is classified into four groups: (1) rich (annual expenses superior to the
75th percentile), (2) comfortable ( annual expense between 50th and 75th percentile), (3)
poor ( annual expenses between 25th and 50th percentile) and (4) very poor (annual expenses
less than the 25th percentile). We assign the following scores for each group: rich = 1,
comfortable = 2, poor = 3 and very poor = 4.
Reputation
The micro-entrepreneur's reliability is used as a proxy to measure this construct (Howorth
and Moro, 2006). The perception of the village chief toward every micro-entrepreneur is used
by Coleman (2006) to gauge reliability. Mayer et al. (1995) and Davies and Prince (2005)
employed three dimensions to measure this construct: (1) competency, (2) integrity and (3)
benevolence. Each dimension has been measured on a scale of 3 points that takes values 1 for
bad, 2 for average and 3 for good. The scale has been purified by the ACP method (KMO =
0.67 and alpha of Cronbach = 0.87) and a factor called " reputation for reliability » which
explains 80% of the variance, has been identified. The factorial score is introduced in the
econometric model.
Entrepreneurial profile
The entrepreneurial profile is measured by the "internal locus of control"5. According to
Rotter (1966), Pandey and Tewary (1979), Cromie and Jhons (1983) and Cromie (1987),
entrepreneurship requires an internal locus control attitude. Levenson (1974) identifies 21
items6, to measure this concept. Each item is quantified on a likert scale basis of five points
going from "completely agree" to "completely disagree". The scale has been purified by the
5 The locus of control emerged with Rotter (1966) and reflects the perception that individuals of the control of
events have in their life. Those who associate the control of events to themselves are said to have an internal " locus
of control ". Those who assign the control to outside powers are said to have an external " locus of control ".
6 At the beginning, the scale comprises 24 items. Three items have been eliminated (at the rate of an item by
dimension). It is the item that measures the perception of the guarantors' control within an organization. These
items cannot be used in our work because guarantors are micro-entrepreneurs and not employees in an organization
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method of ACP (KMO = 0.85 and alpha of Cronbach = 0.91) and a factor called "internal"
has been identified which explains 79.5% of the variance. The factorial score is introduced in
the econometric model.
Customer relationship duration
Customer relationship duration is generally measured by the number of years during which
the client maintains a sustainable relationship with the financial institution [Angelini et al.,
1998; Berger and Udell, 1995 and Peterson and Rajan, 1994]. However, In micro-finance,
considering the nature of the micro-credit that is generally distributed on the short term basis,
most researchers employed the number of credits incurred by the micro-entrepreneur within
the MFI [Honlonkou et al. 2006 and Lanhas, 2002]. In this research, we will proceed in the
same way and the customer relation will take the value 0 if the micro-entrepreneur did not
run up a credit at the institution, the value 1 if he ran up credit, the value 2 if he ran up 2
credits and the value 3 if he run up 3 credits or more.
Guarantee
Honlonkou et al. (2006) have measured this variable in the area of micro-finance. They used
the percentage of material collateral values over the loan amount as a measurement of this
variable. In Tunisia, the legal framework7 stipulates that the micro-credit should be granted to
micro-entrepreneur who could not provide any corporal collateral. To get around this rule, the
MFI requested a “guarantor” as a substitute of corporal collateral. Such third party will
guarantee by bills the pay back of the loan. Thus, this variable will take the zero value if the
applicant could not provide a guarantor and the value of 1 value otherwise.
Finally, the age of the micro-entrepreneur and the size of the association are respectively
measured by the number of years and the log of the credit portfolio, whereas the remaining
variables are introduced as binary variables as follows: Gender = 1 for female and zero for
male; Sector of activity = 1 for agriculture and zero for the remaining sectors; level of
education = 0 if the micro-entrepreneur is analphabet and 1 otherwise; and training = 1 if the
person has already attended a technical training and zero otherwise.
Data collection
In this research, the questionnaire has been used as an instrument of data collection. This
questionnaire has been managed to the sample of the survey, during the months of June and
July 2007. The field work mobilized 8 investigators. The relative questions to the variable
poverty have been developed together with the experts of the National Institute of Statistics,
which proceeds every five years to the assessment of the standard of living of Tunisians and
to measuring poverty. The questionnaire has been tested during two days with a group of
micro-entrepreneurs and adjustments have been carried at the level of questions relative to
the variable locus of control, to take into consideration the cultural sensitivities.
Specification of the econometric model
In this work, the variable to explain is the financial exclusion that can take two modes:
0 if the person is financed
yi (Exclusion) =
1 if the person is rationed by the credit
7 Laws 99-67 and 99-70
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The phenomenon to study is therefore discreet and the variable that describes it is
dichotomic. The specification of a traditional regression model is therefore misfit. The resort
to a binary Logit model proves to be adapted to our econometric analysis.
IV. RESULTS
Tests of correlations and associations have been conducted before the specification of the
model to check if there is no problem of multi co-linearity between the explanatory variables.
Results of these tests indicate that values of coefficients are low and still lower to 0.5.
Therefore, there is no problem of multi co-linearity between the retained explanatory
variables in this research.
The process of modeling has been achieved in several stages. We opted for a method of an
ascending step-by-step regression. The explanatory variables and those of control have been
introduced progressively in the model. Ten equations have thus been specified in order to
study the effect of different explanatory variable combinations on the probability of the
exclusion. The retained model is judged as having a good quality of adjustment (Pseudo R² of
0.68). The remaining part of this section will be dedicated to the interpretation of the sign and
the point of coefficient significance obtained and presented in Table 3.
Results reveal the existence of a negative and significant relation to the point of 5% between
the variables "gender" and "rationing". It implies that women have less risk to be excluded
that men. This result confirms contributions of Schreiner (2000), Morduch (1999a) and
Khandar (1998) that explain that the MFI prefer to grant micro-credits to women rather than
to men. It also confirms hypothesis 7 according to which the gender has an effect on the
rationing. The introduction of the control variables "training * gender" and "level of
education * gender" (equation 2) shows that the level of education does not have any effect
on the exclusion of women but that the training increases their risk to be rationed. This result
is explained by the fact that the trained women want to get involved in activities that are not
priority to associations. In fact, 43% of trained women want to invest in non-agricultural
activities that are not priority to the association. Indeed, more than 62% of refused credit
requests concern non-agricultural projects.
Poverty proves to be without an explanatory power on the probability of credit rationing. It
shows that, on one hand, the poor do not endure a discriminatory behavior on the part of
associations, but that on the other hand, they do not constitute a privileged clientele to these
associations. The introduction of control variables in the relation (" age * poverty ", " level of
education * poverty " and " size of the institution * poverty ") (equation 3) does not change
anything of the results. This report reveals that poverty does not constitute a criterion of
selection for associations of development and leads to reject the third hypothesis of research.
Nevertheless, it is convenient to push the analysis further to know if the behavior of
associations is the same towards the different socio-economic categories. To do that, we
proceed to the crossed-sorting between the variables poverty and rationing.
Results of Table 2 show that the poor recorded the highest rate of financed people, which is
78.4%. They are directly followed by the comfortable (75.7%). The very poor, with a
financing rate of 66.7%, are more confronted to the problem of rationing than these first two
groups. However, they are more disposed to be financed than the rich that record a rate of
financing of 63.9%. So, these results show that the poor and the very poor are not confronted
to a problem of rationing from associations.
Table 2 : Crossed sorting between « poverty » and « rationing »
FINANCE RATION TOTAL
Rich 63.9% 36.1% 100%
Comfortable 75.7% 24.3% 100%
Poor 78.4% 21.6% 100%
Very poor 66.7% 28.8% 100%
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The results of equation 4 show that the sector of activity does not have an impact on the
financing decision of the association and then invalidate the fifth hypothesis of research. This
result is contradictory with contributions of Beck and De La Torre (2006), Beck et al. (2006)
and Baydas (1994) who explain that agriculture is a risky activity and that banks and the MFI
avoid financing. By introducing the variable "guarantor" (equation 7), the relation between
"agriculture" and "exclusion" becomes significant and negative. This indicates that
agriculture has the same negative effect on the rationing but that this effect is not robust. On
the economic side, the interpretation of this result proves to be difficult, so much that there is
no association between the variables "agriculture" and "guarantor". However, it is to remind
that associations of local development constitute one of the privileged mechanisms of the
state to finance the small agriculture. In the annual financing contract that links the
association to the BTS, the latter requires that a great part of credits be granted to the
agricultural activity.
The first and the second hypotheses of research are respectively confirmed and rejected. In
fact, the "reputation for reliability" has a negative impact on the rationing of the credit
whereas the "locus of internal control" has a positive impact (equations 7 and 9). These
explain that, on the one hand, associations of development rely on the local information to
attenuate problems linked to the asymmetry of information, and on the other hand, the local
socio-cultural environment does not encourage a good perception of people having some
entrepreneurial characteristics. However, with the introduction of the variable "guarantor",
the effects of the "reputation for reliability" and of "locus of internal control" are not more
significant (equation 10). This gives rise to two interpretations. The first is that the
"reputation for reliability" of a micro-entrepreneur does not have an effect on his/her access
to financing if this one does not find a guarantor. The second is that the risk linked to the
entrepreneurial profile is attenuated when the micro-entrepreneur is guaranteed by a
guarantor.
The estimation of the coefficient of the variable "duration of the customer relationship"
shows that the longer the relationship, the less the micro-entrepreneur risks to be rationed by
the credit (equations 8, 9 and 10). This result confirms the contributions of Honlonkou (2006)
who explains that for a micro-entrepreneur, an evolved relation with the institution of microfinance
is synonymous of a more lasting access to the credit. He joins as well the results of
Petersan and Rajan (1994), Angelini, Salvo and Ferri (1998), Lehmann and Neuberger (2001)
and Bodt, Lobez and Statnik (2005) who showed that the duration of the customer
relationship has a positive effect on the access to financing.
However, the resort to the crossed sorting between the two variables (Table 4) shows that
nearly 90% of micro-entrepreneurs having an experience, or more, with the association are
guaranteed by guarantors. This percentage decreases to 32% for those who do not have any
experience. The re-introduction of the variable "guarantor" at the same time as the variable
"duration of the customer relationship", shows that the "guarantor" always has a negative and
significant effect. Besides, this re-introduction permitted to improve the explanatory power of
the model considerably.
Thus, the found results permit to conclude that the effect of the "duration of the customer
relationship" on the exclusion is extensively influenced by the effect "guarantor". To study
the direct impact of the variable "duration of the customer relationship" on the probability of
the exclusion, it is convenient to eliminate the effect "guarantor". For that, we started with
decreasing the variable "duration of the customer relationship" in relation to the variable
"guarantor". The results show that the "guarantor" has a significant effect (at 1%) and strong
(? = 0.77) on the duration of the customer relationship. The residue (the mistake which is not
explained) of the decline has thereafter been kept as a new variable called "excessrc". This
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
651
one corresponds to the variance of duration of the customer relationship not explained by the
variable "guarantor".
To determine if the duration of the customer relationship has an effect on the probability of
the exclusion even in the absence of the effect "guarantor", there is possibility to estimate the
coefficient of the Logit model for the variable "excessrc". The result (Table 5) reveals the
existence of a negative and significant relation at 1%. The elimination of the effect
"guarantor" has not therefore changed anything to the relation between the variable "duration
of the customer relationship" and the variable "exclusion". This result implies that the
"guarantor" is not the only explanatory variable of the probability of the exclusion. The
duration of the customer relationship has a negative effect on the exclusion, and this
independently on the effect "guarantor". Therefore, hypothesis 4 is confirmed.
To be sure of the quality of adjustment of the last specified model (equation 10), we consider,
in addition to the R² of McFadden (which is equal to 0.68)8, another indicator which is the
quality of prediction9. The table of prediction, presented in Appendix, reveals a prediction
rate of 95.21%.
This indicates that in 95% of cases, values 0 and 1 of the dependent variable have been
predicted well. A more thorough analysis of the table of prediction shows that for the
excluded, 36 cases out of 42 have been predicted well whereas for the financed, 103 cases out
of 104 have been predicted well.
Finally, it proves to be interesting to measure the degree of impact of each of the explanatory
variables on the exclusion. Results of the marginal impact calculations (table 6) show that
women have nearly 30% of risk less than men to be rationed by the quantity of the credit. The
trained women have, as for them, 60% of risk in addition to be excluded than non-trained
women. The increase of the duration of the customer relationship by experience decreases by
25% the risk of the exclusion. An improvement of 10% of the "reputation for reliability" in
the community decreases the risk of the exclusion by 3.5%. An increase of 10% of
entrepreneurial skills results in an increase of 5.4% in the risk of exclusion. In short, microentrepreneurs
who are guaranteed by a guarantor have 83% less risk to be excluded.
Conclusion
Results of this work bring us to make five main conclusions. The first is that guarantor's
absence constitutes the main barrier to the access to the micro-credit. The second is that the
"duration of the customer relationship" has a negative effect on the exclusion but does not
result in an improvement of conditions of financing, notably in term of guarantor. The third is
that, contrary to what has been anticipated, the agricultural sector does not have a positive
impact on the decision of rationing of the credit. Finally, the last two conclusions permit to
position some results of Mejdoub and Mamoghli (2009) in a better way. On the one hand, the
"reputation for reliability", even though taken into consideration in the decision of financing,
loses all its significance in guarantor's absence. On the other hand, the risk that represents,
paradoxically, the entrepreneurial profile for associations, is attenuated when the borrower is
guaranteed by a guarantor.
8 The R² of McFadden remains a statistic that is not very useful for the interpretation of results. In fact, in logistical
decline, it is not possible to calculate the variance in the dependent variable that is explained by the independent
variables.
9 The explanation of the prediction quality is the following. If event yi = 1 occurs when the latent variable takes a
value higher than the C point (fixed at 0.5 by default in the Logit model) and if event yi = 0 occurs when the latent
variable either takes a value equal or lower than the C point, then it is convenient to compare these predictions to
the true values held by yi.
678
Table 3 : Estimation of coefficients of the model
Equation
1
Equation
2
Equation
3
Equation
4
Equation
5
Equation
6
Equation
7
Equation
8
Equation
9
Equation
10
Gender -.86**
(-2.11)
-1.70**
(-2.12)
-1.80**
(-2.31)
-1.89**
(-2.36)
-1.56*
(-1.92)
-1.38*
(-1.67)
-1.59*
(-1.79)
-2.23***
(-3.10)
-1.88***
(-3.74)
-3.35***
(-3.34)
Level of education - .63
(1.37)
.60
(1.30)
.57
(1.25)
.68
(1.37)
.75
(1.47)
.99
(1.48)
.56
(0.89)
- -
Level of
education*gender
- -.06
(-0.07)
.06
(0.07)
.00
(0.01)
.03
(0.04)
-.06
(-0.07)
-.56
(-0.57)
.35
(0.38)
- -
Training - -.06
(-0.13)
-.00
(-0.01)
-.06
(-0.12)
-.01
(-0.02)
.12
(0.20)
.41
(0.62)
-.44
(-0.72)
- -
training*gender - 1.84*
(1.91)
1.88*
(1.91)
1.94**
(1.98)
1.78*
(1.81)
1.40
(1.45)
.99
(0.83)
1.93**
(1.96)
1.35*
(1.68)
3.26***
(3.10)
Poverty -.084
(-0.48)
.07
(0.38)
-.84
(-0.44)
-.92
(-0.49)
.16
(0.08)
.50
(0.24)
1.98
(0.79)
-.24
(-0.96)
- -
Age - - .02
(0.62)
.03
(0.75)
.043
(0.92)
.05
(0.94)
.035
(0.66)
- - -
Age*poverty - - -.00
(-0.50)
-.01
(-0.56)
-.012
(-0.67)
-.01
(-0.60)
-.015
(-0.78)
- - -
Association
size*poverty
- - .37
(0.64)
.42
(0.72)
.16
(0.29)
.10
(0.17)
-.34
(-0.46)
- - -
Agriculture - - - -.46
(-1.14)
-.45
(-1.04)
-.60
(-1.29)
-1.54**
(-2.20)
.57
(0.94)
- -
Duration of customer
relation
- - - - - - - -2.64***
(-3.35)
-2.43***
(-3.28)
-2.56***
(-3.06)
Guarantor - - - - - - -4.62***
(-5.28)
- - -5.14***
(-3.46)
Reputation for
reliability
- - - - - -.62***
(-2.72)
- - -.45*
(-1.67)
-.36
(-1.35)
Locus of internal
control
- - - - .70***
(3.31)
.66***
(2.88)
.38
(1.40)
.58***
(2.79)
.58***
(2.63)
.56
(1.50)
Constant -.41
(-0.85)
-1.14***
(-1.71)
-2.45
(-1.16)
-2.54
(-1.23)
-
3.353299
(-1.62)
-4.00**
(-1.72)
.95
(0.36)
2.29**
(2.54)
1.90*
(2.83)
6.20*
(3.10)
Pseudo R2 0,03 0,075 0,08 0,09
0,15 0,2 0,48 0,49 0,49 0,68
***Signification at 1% ** Signification at 5% * Signification at 10%
Table 4 : Crossed sorting between « guarantor » and « duration of the customer
relationship »
HAS NO GUARANTOR HAS A
GUARANTOR
TOTAL
No previous credit contracted 67.9% 32.1% 100%
A previous credit contracted 13.0% 87.0% 100%
Two or more credits contracted 9.4% 90.6% 100%
Table 5 : Crossed sorting between « guarantor » and « duration of the customer
relationship »
COEFFICIENT Z SIGNIFICATION
Excessrc -1.451774 -2.82 ***
Constant -1.194612 -4.05 ***
***Signification at 1%
Table 6 : Estimation of marginal impacts of explicatory variables
dy/dx Z SIGNIFICATION
Gender -.283901 -2.53 **
Training * Gender .6030231 2.91 ***
Duration of customer
relationship
-.2502157 -4.43 ***
Guarantor -.8340719 -5.94 ***
Reputation for reliability -.03575 -1.17
Internal .0548742 1.72 *
***Signification at 1%** Signification at 5%* Signification at 10%
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
679
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Appendix: Tableau of prediction for the specified logit model.
Logistic model for excldum
-------- True --------
Classified | D ~D | Total
-----------+--------------------------+-----------
+ | 36 1 | 37
- | 6 103 | 109
-----------+--------------------------+-----------
Total | 42 104 | 146
Classified + if predicted Pr(D) >= .5
True D defined as excldum != 0
--------------------------------------------------
Sensitivity Pr( +| D) 85.71%
Specificity Pr( -|~D) 99.04%
Positive predictive value Pr( D| +) 97.30%
Negative predictive value Pr(~D| -) 94.50%
--------------------------------------------------
False + rate for true ~D Pr( +|~D) 0.96%
False - rate for true D Pr( -| D) 14.29%
False + rate for classified + Pr(~D| +) 2.70%
False - rate for classified - Pr( D| -) 5.50%
--------------------------------------------------
Correctly classified 95.21%
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683
Cost of equity in emerging markets. Evidence from Romanian listed
companies
Costin Ciora
Teaching Assistant
Department of Economic and Financial Analysis
Bucharest Academy of Economic Studies, Romania
[email protected]
Acknowledgements
This article is a result of the project POSDRU/6/1.5/S/11 “Doctoral Program and PhD Students in the education
research and innovation triangle”. This project is co funded by European Social Fund through The Sectorial
Operational Programme for Human Resources Development 2007-2013, coordinated by The Bucharest Academy of
Economic Studies.
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
684
Abstract
The cost of equity has been for decades the purpose of the work of many famous economists that
embraced the challenge of finding the appropriate method of calculation. From models and
formulas to real application and impact, we built our research on Romanian listed companies, as
a quest to both the theory and practice of one of the most controversial indicator in the financial
world: the cost of equity. Through this article, our purpose is to provide clear examples for
calculating the cost of equity for emerging countries, which could be useful for both
academicians and practitioners.
Keywords: cost of equity, cost of capital, asset pricing
I. Introduction
In an unpredictable financial world, the theory could face difficulties of explaining evident
differences to practice when it comes to cost of equity. Some models could be seriously affected
by the evolution of the global financial crisis started in 2008. For example, the common
calculation of the market return, when calculating the market risk premium, could be affected by
the negative evolution of the markets. Some authors claimed that the use of arithmetic averages
or geometric average can show us the value of the market return. The evolution of the financial
markets from 2008 till today can provide us sufficient understanding that this method has
important limitation. Another example could be the value of volatility coefficient – beta which
can have a negative value due to the opposite evolution of a company compared with the stock
index which was taken as a benchmark. The quest for calculating the cost of capital has multiple
applications, from the stock market, valuation or performance measurement. Starting from these
ideas, we provided relevant information from the Romanian financial market.
The framework for the cost of equity
For the financial point of view, the capital represents the sum of financial resources needed by
companies, governmental institutions for current operations or for financing or investments. The
cost of capital represents the opportunity cost of financial resources (both equity and debt), and
required return for the investors. The cost of capital is the minimum expected return by the
investors for the capital provided by them to the company. Another application is using the cost
of capital in calculating value creation metrics such as Economic Value Added, one of the most
complex indicator of company’ performance.
The paper of Franco Modigliani and Merton Miller (1958),” The cost of capital, corporation
finance and the theory of investment”, presents that the market value of any company is
independent from the capital structure, and will be the same for different leverage ratios.
Moreover, the same authors consider that the average value of the cost of capital is independent
from the capital structure. The authors support this argument, starting from simple hypothesis.
Thus, investors must trade shares without restrictions and can borrow money in the same
conditions as the company. The financial markets must by efficient, so the shares must have a
price proper to the information held by investors. The theory of the two famous economists
takes into consideration neither the supplementary cost for supplementary borrowings, nor the
distorted taxes, as Brealey, Myers and Marcus stated (2005). In the real activity of a company,
the mix of equity and debt for financing can influence the value of the company. Damodaran
(1994) tells us that companies take into consideration the decision for capital structure, and
potential influences for the value of the company (level of debt, agency cost, borrowings
benefits).
Invested capital is the sum of equity and financial debt, as the model:
K = E + D (1)
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
685
K= Invested capital
E = Equity
D = Financial debt
The value of equity and financial debt can be the book value from the financial statements or the
market value calculated through the valuation of the company. Calculating the cost of capital is
through the weighted average cost of capital, and it is complex due to the need of calculating the
cost of equity and cost of debt.
Thus, the weighted average cost of capital is obtained through the following model
presented by Pratt (2002):
E D
WACC E Ke D t Kd
+
+ -
= * *(1 )*
(2)
Ke = cost of equity
Kd = cost of debt
t = income tax
II. Empirical Analysis for calculating the cost of equity: Romanian listed companies
We will focus on calculating the cost of equity on Romanian listed companies, as a way of
expressing the methods used in an emerging market such as Romania. Our study was developed
taking information from 15 listed companies at the Bucharest Stock Exchange for the period
2005-2009, a period important from the perspective of the global impact of financial crisis on
nations’ economies.
Companies were chosen from a set of criteria: companies that were listed during 2005-2009;
strategic sectors such as energy or chemical industry; high capitalization level
The companies were grouped as follows: Energy including: extractive, transport, storage of oil
and natural gas; Pharmaceutical sector; Metallurgical industry; Chemical industry; Aerospace
industry. The companies chose represented 23.43% of the total capitalization of the Bucharest
Stock Exchange and the end of 2009. In the chosen companies we can find the largest company
in Romania, which had 17.61% of the total capitalization of the Bucharest Stock Exchange. For
calculating the cost of equity, we used the most used model, mainly CAPM (Capital Asset
Pricing Model). This model was proposed independently by J. Treynor (1961, 1962), J.Lintner
(1965), J. Mossin(1966), but underpinner by the wpork of William Sharpe (1964) through his
article “Capital Asset Prices: a theory of market equilibrium under conditions of risk”. The
subject was also researched by economists Harry Markowitz (1952). In a research of John
Graham and Campbell Harvey (2001) over 392 companies in USA (including companies from
Forbes 500) showed that 73.5% from the chief financial officers questioned use CAPM to
estimate the cost of equity.
CAPM states that if all investors follow the market portfolio, the risk premium required by them
will be proportional with beta volatility coefficient, and adding the risk-free rate.
*( ) f m f Ke = R +ß R - R
(3)
Rf = risk-free rate
ß = beta volatility coefficient
Rm = market return
Rm - Rf = market risk premium
CAPM starts from the following assumption as Pratt(2002) presents: investors are risk averse;
rational investors intend to own diversified portfolio of shares; all investors have identical time
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686
horizons (period of owning the shares); all investors have identical expectation for returns and
the way capitalization rates are generated; there aren’t any transaction cost; there aren’t taxes for
investments;
A. Determining the risk-free rate
The risk-free rate must not reflect specific risk, thus it is found in the yield of governmental
bonds. Companies have different ratings, because they have different levels of risk. The
countries that must be taken into consideration are countries with AAA rating (the highest rank
from international rating organizations). Using a long-term maturity (more than 10 years) offer a
real value of this rate. Authors Anghel, Oancea Negescu, Anica-Popa and Popescu
(2010)consider that this period is recommended because it is similar with the period of free cashflow
prevision of a company. Moreover, the currency used to calculate this rate must be the same
with the currency in which the cash-flows are calculated and the financial statements are
presented. For Romania different authors consider the risk-free rate as the Romanian
governmental bonds yield to maturity. We consider that this approach do not reflect the relation
between risk and risk-free rate.
To estimate this rate we used the following method as Ciobanu (2010) presents: taking as a
benchmark the risk-free rate expressed in euro (German governmental bonds with a maturity of
10 years) and adding the difference between the inflation rate expressed for lei (Romania’s
currency) and inflation rate for euro, stated by Damodaran (2010):
Rf lei = Rf euro + (Ri lei – Ri euro) (4)
For the risk-free rate of euro we took into consideration the yield to maturity of bonds issued by
Germany 10 years maturity, because the country rating for Germany is AAA (Moody’s). As we
see in Table I, the risk-free rate evolved from 36.90% in 2001 (high value because of inflation)
to 8.61% in 2009.
The applicability of this calculation is very high for emerging markets that have lower country
ratings.
B. Determining Beta volatility coefficient
Beta volatility coefficient (ß) is one of the most used instruments to measure company risk
related to the investment in shares. Beta multiplies the risk premium for measuring the
investment in the company’s shares. Thus, companies with a beta higher than 1 present a
supplementary risk than the market portfolio (represented by the benchmark index), companies
with beta equal to 1 present a similar risk with the market, and companies with beta less than 1
are less riskier than the market portfolio.
Beta volatility coefficient is calculated as follows:
?? ?? ?????? ??????,??????
???????????? (5)
?????? ?????? ,??????= covariance of “i” share return and index return of the stock market”m”
???????????? = variance of the index return from stock market “m”
For different levels of financial debt, the level of beta is different, because of extra risk.
Thus, beta volatility coefficient must be adjusted as follows, as presented by Pratt (2002):
???? ?? ???? (1+(1-t)(D/E)) (6)
Unde:
????= leveraged beta
????=unleveraged beta
t = income tax rate
As the weight of financial debt in invested capital is higher, we will have a higher risk reflected
through a higher level of the beta volatility coefficient, and through a higher level of the cost of
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equity. A higher level of debt in the capital structure represents a supplementary risk for
investors, and therefore higher expectations related to the required return, as it is shown in table
II. At the sector level, through the beta coefficient we can compare different sectors to obtain
important evidence of the risk category for the companies. In table III we can see the level of the
unleveraged beta for the sectors in which we can find the selected companies.
Companies from the energy sector have a beta volatility coefficient higher than 1, expressing the
supplementary risk for these companies, in a period of uncertainty and fluctuation in the oil and
natural gas market. A similar situation can be seen in the chemical industry (with an exception in
2005), and in companies from the pharmaceutical industry. Companies from the metallurgical
industry and aerospace industry have less volatility than the market, thus a variation in the
market will lead to a slower modification of the prices of the companies in these sectors. This is
shown in figure 1.
C. Determining the market risk premium
For calculating the market risk premium for 2005-2009, we used the BET-C index from the
Bucharest Stock Exchange as a benchmark for comparison.
The market risk premium is calculated as follows:
Market risk premium = Rm – Rf (7)
Rm = return of stock market ”m”
Rf = risk-free rate
There are several ways of calculating the market risk-premium: using results from questionnaires
from the stock market in which investors claim the required return; using the historical date from
the stock market and through average or geometrical calculation establishing the return of the
market, and thus the market risk premium; using a market risk premium from a developed
market by referring to the benchmark index or governmental bonds from that country; using the
benchmark index from the emerging country and benchmark index from the developed market
We used the last method as it is one of the best ways of calculating the risk premium for
Romania’s case. The use of average or geometrical mean could have provided us negative
returns for 2008-2009 and thus an great negative influence on the cost of equity.
Damodaran (2010) considers that in this case the model becomes:
Emerging MRP = Developed MRP x
?????????????????? ?????????????? ?????????????????? ??????????
???????????????????? ?????????????? ?????????????????? ??????????
(8)
MRP= market risk premium
s = standard deviation
By using the example of USA as developed market, for Romanian stock market, the model
becomes:
(Rm – Rf)RO = (Rm – Rf)US x
??????????
????&????????
(9)
(Rm – Rf)RO = market risk premium for Romanian stock market
(Rm – Rf)US = market risk premium for USA stock market
sBETC = standard deviation of BET-C return
sS&P?????? = standard deviation of S&P 500 return
Table IV provides a closer look on the calculations for the period 2005-2009.
From the above calculations, we can observe an average of 2 for the ratio between the standard
deviation of BET-C return and standard deviation of S&P 500 return, expressing the
supplementary risk from the Romanian capital market compared with the more developed
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688
American market. The market risk premium for Romania’s stock market will be calculated as a
product between market risk premium from the developed country (USA) and the ratio of their
benchmark index’s standard deviation (previously calculated). The results are presented in Table
V, and so is the value for the market risk premiums for Romania between 2005 and 2009.
D. Calculating the cost of equity
Based on the information provided for the risk-free rate, beta volatility coefficient and market
risk premium, we calculated the value for the cost of equity which can be seen in table 6.
From the date calculated in table VI we can see some important opinions.
The average value of the cost of equity increased from 19.33% in 2005 to 19.65% in 2006, based
on the increase of uncertainty for investors. The year 2007, which was known as a economy
boom year, lead to a decrease of the average cost of equity for the selected companies to 18.42%
(which is also the minimum of the 5 year period). The effects of the financial crisis soon were
represented in the financial indicators, leading to an increase to 21.75% of the average cost of
equity in 2008. In this year, investors felt the extra risk, and thus, increase the required return.
In 2009, the average cost of equity decrease to a value of 19.66%.
The level of the cost of equity is useful for representing the SML’s (Security Market Line) for
2005-2009. The oX line represents the beta volatility coefficient, while oY line is the expected
return or the cost of equity, calculated through CAPM. In figure 3 we represented the SML for
the largest company in Romania – OMV Petrom (energy sector).
III. Conclusions
The article examines the methods used for calculating the cost of equity for companies from
emerging countries. Emerging markets have the disadvantage of having more risk that developed
countries presented by the country ratings. Thus the risk-free rate must be calculated in a
customized method, as well as the market risk premium. This articles offer eloquent opinions and
facts for academicians and practitioners. Underpinning, as our quest provided deeper
understanding for the cost of capital, the result offers us a practical image of how the cost of
capital should be calculated. The cost of equity represents a challenging topic that will continue
to bring ideas.
References
Anghel I., Oancea Negeascu M., Anica Popa A., Popescu A.M, (2010).Business Valuation,
(Economic Publishing House, Bucharest)
Brealey, R.A., Myers, S.C. and Marcus, A.J.,2005. Fundamentals of corporate finance (McGraw-
Hill)
Ciobanu, A.(2010) Discount rate Workshop, Romanian Association of Valuators
Damodaran, Aswath, 1994. Damodaran on Valuation, Security analysis for investment and
corporate finance. (Wiley & Sons)
Damodaran, Aswath, 2010. Applied Corporate Finance (Wiley & Sons)
Damodaran, Aswath, 2010. Corporate finance courses, Webcast,
http://pages.stern.nyu.edu/~adamodar/
Graham, J.R.,and Harvey, C.R., 2001.The theory and practice of corporate finance. Evidence
from the field, Journal of Financial Economics, no. 60
Lintner, J. 1965. The valuation of risk assets and the selection of risky investments in stock
portfolios and capital budgets, Review of Economics and Statistics, 47 (1), 13-37.
Markowitz, H.M. 1952. Portfolio Selection. The Journal of Finance 7 (1): 77–91
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
689
Modigliani, Franco, and Merton H. Miller, 1958. The cost of capital, corporation finance and the
theory of investment ,The American Economic Review, vol. XLVIII, 3,411-433
Mossin, J. 1966. Equilibrium in a Capital Asset Market, Econometrica, Vol. 34, No. 4, pp. 768–
783.
Pratt, S.P., 2002.Cost of Capital, Estimation and applications (Wiley & Sons)
Sharpe, W., 1964. Capital asset prices: a theory of market equilibrium under conditions of risk.
Journal of Finance, 19:425-42.
Treynor, J. L.1961. Market Value, Time, and Risk. Unpublished manuscript.
Treynor, Jack L.1962. Toward a Theory of Market Value of Risky Assets. Unpublished
manuscript
Table I - Calculating the risk-free rate for Romania for 2001-2009
2001 2002 2003 2004 2005 2006 2007 2008 2009
Yield to maturity of bonds
issued by Germany with 10
years maturity
4,80% 4,78% 4,07% 4,04% 3,35% 3,76% 4,02% 3,20% 3,32%
Inflation rate - LEI 34,50% 22,50% 15,30% 11,90% 9,00% 6,56% 4,84% 7,85% 5,59%
Inflation rate EURO 2,40% 2,30% 2,10% 2,10% 2,20% 2,20% 2,10% 3,30% 0,30%
Risk-free rate - Romania 36,90% 24,98% 17,27% 13,84% 10,15% 8,12% 6,76% 7,75% 8,61%
(Sources: Eurostat, INSSE, National Bank of Romania, own calculation)
Table II - Unleveraged and leveraged beta as averages for the selected companies
*the symbols for the companies are symbols used for the Bucharest Stock Exchange
Table III - Unleveraged Beta volatility coefficient grouped by sector
Sector: 2005 2006 2007 2008 2009
Energy 1.30 1.26 1.11 1.14 1.24
Metallurgical industry 0.52 0.70 0.81 0.86 0.87
Chemical industry 0.80 1.07 1.23 1.24 1.14
Pharmaceutical industry 1.01 1.16 1.14 1.18 1.11
Aerospace industry 0.30 0.60 0.76 0.95 0.92
Table IV - Standard deviation ratio for BET-C and S&P 500 ‘returns
s BET- C s S&P500 s BET- C / s S&P500
2009 10.05% 4.65% 2.163
2008 9.47% 4.35% 2.178
2007 8.03% 3.84% 2.092
2006 8.35% 4.00% 2.085
2005 8.53% 4.31% 1.977
Type
Unleveraged beta Leveraged beta
2005 2006 2007 2008 2009 2005 2006 2007 2008 2009
Minimum 0.24 0.12 0.1 0.4 0.53 0.24 0.16 0.11 0.42 0.56
Average 0.867 0.978 1.025 1.066 0.987 0.966 1.119 1.165 1.288 1.202
Median 0.97 1.03 1.08 1.06 0.97 1.04 1.1 1.13 1.4 1.2
Maximum 1.69 1.64 1.41 1.44 1.62 1.75 1.9 2.32 2.2 1.91
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Table V - Market risk premium
MRP USA s BET- C / s S&P500 MRP Romania
2009 4.50% 2.163 9.73%
2008 5% 2.178 10.89%
2007 4.79% 2.092 10.02%
2006 4.91% 2.085 10.24%
2005 4.80% 1.977 9.49%
Table VI - Cost of equity for selected companies between 2005 - 2009
Cost of equity 2005 2006 2007 2008 2009
Minimum 12.46% 9.76% 7.88% 12.31% 14.01%
Average 19.33% 19.65% 18.42% 21.75% 19.66%
Median 20.05% 20.30% 18.09% 23.03% 19.33%
Maximum 26.78% 27.56% 29.97% 31.72% 24.84%
Figure 1 Unleveraged beta by sector
0.00
0.50
1.00
1.50
2005 2006 2007 2008 2009
Energy
Metallurgical industry
Chemical industry
Pharmaceutical industry
Aerospace industry
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691
Figure 2 Comparison between USA market risk premium and Romanian market risk
premium
Figure 3 SML during 2005-2009 for OMV Petrom
9.49% 10.24%
10.02%
10.89%
9.73%
4.80%
4.91%
4.79% 5% 4.50%
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
2005 2006 2007 2008 2009
Romania's market risk
premium
USA's market risk
premium
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
0 2 4 6 8 10
Expected return
(cost of equity)
Beta
K 2009
k 2008
k2007
k2006
k2005
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692
Corporate Events’ Effect on Stock Returns: Evidence from Athens Stock
Exchange
Aristeidis Samitas
Univeristy of the Aegean
Dept of Business Administration
Business School
8 Michalon Street, Chios, 82100, Greece
[email protected]
Dimitris Kenourgios
University of Athens
Faculty of Economics
5 Stadiou Street, Office 115
Athens 10562, Greece
[email protected]
Ioannis Tsakalos
University of the Aegean
Dept. of Business Administration
Business School
8 Michalon Street, Chios, 82100, Greece
[email protected]
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
693
Abstract
This study examines firm’s stock returns’ behaviour, when they announce corporate events like
management change, collaborations and stock repurchase. It examines how this change is
portrayed in firms’ stock prices returns. The methodologies used are the methodology of event
study analysis and bootstrap methodology. Companies selected belong to eight different sectors
of Athens Stock Exchange with different Stock Exchange value in order to get a more general
picture that does not only represent one sector which can be influenced individually from
accidental factors. The sample constitutes forty firms listed in Athens Stock Exchange. Results
indicate that corporate events’ impact is important and a key for enterprises to follow new
challenges and create financial value. This paper provides evidence on the impact of corporate
governance on stock returns in Greece. The implication is that corporate events create financial
value.
Keywords: Corporate governance; Cumulative abnormal returns; Athens stock exchange, Event
study analysis, Bootstrap methodology
Jel: G14, G15
I. Introduction
In the beginning of the new century, Athens Stock Exchange’s (ASE) returns led to euphoria
after the abrupt rise in 1999 when unjustifiable stock prices reactions were observed. Stock
prices’ increase led the sector indices and Athens Stock Exchange general index to a frantic
ascendant course till September 1999. Then, a sudden change in the investment climate
influenced negatively on stock prices returns and the aforementioned increase finally stopped.
In this study, the examined period concerns the period 2000-2006 when ASE had a smoother
fluctuations than the last years when we observe an extremely volatile market. During that
period, ASE was characterized mature, stock prices’ reactions in various events were more
equitable and represent a physiologic development of enterprising incidents. On the other hand,
the last few years, all markets face huge problems and high volatility follow previous years’
stability. The need to understand corporate events impact on stock returns is the reason why this
research exempts the volatile period.
The purpose of this paper is to examine the relationship between corporate events and eight
sectors share prices’ returns in Athens Stock Exchange. There are 97 events that took place in the
examination period (Table I). Moreover, this study proves that these issues are very important,
especially in this new economic environment for enterprises.
Please Insert Table I about here
The rest of the paper is structured as below. The next section refers to the literature review in
order to contribute our study with the relevant literature and section 3 describes the methodology
used. Moreover, section 4 outlines the data used to this study and section 5 provides the
empirical results and the evidence of their robustness. Finally, paper is concluded by section 6
which outlines the conclusions of this research.
II. Literature Review
Three centuries ago, Adam Smith made the question of companies’ property segregation and
management. However, shareholders and managers have often refuted opinions and interests.
Managers follow their own policy which naturally influences also the shareholders. Allen and
Gale (2001) support that managers’ interests keep pace with those of shareholders, while ‘’big’’
International Research Journal of Applied Finance ISSN 2229 – 6891
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694
shareholders try to develop their earnings without taking care the ‘’small’’ shareholders’ profit.
This is also a huge problem for big companies according to La Porta et al. (1998).
Moreover, Ascioglu et al. (2005) consider that the fluidity is bigger when feeble corporate
governance prevails. This opinion has also been supported by Becht et al. (1998), while
Scholtens and De Wit (2004) support that the statements on fusions influence positively the
returns, as the corresponding statements on repurchases.
Coopera et al. (2005) support that the changes, even in companies’ name, show that these
companies follow the new challenges and attribute positively in their participial value, without
proceeding in big investments. This opinion comes to support also, that companies by entering
new markets, as it is the electronic market, may influence positively their stock prices.
Regarding Greek literature review, studies are very interesting. Athanassoglou et al. (2005),
stated that mergers and acquisitions’ announcement effects on Greek bank stock returns lead to
positive stock returns. One year before, Athanassoglou and Brissimis (2004) made another study
and found similar results.
Moreover, Mylonidis and Kelnikola (2005) note that announcements for mergers and
acquisitions create value on a net basis at the banking sector. Manasakis (2005) approved that
after a merger or an acquisition announcement, we take statistically significant CAR’s. Alexakis
et al. (2006) found that corporate governance reduces the enterprises financial value’s volatility.
Finally, Tsipouri and Xanthakis (2004) approved that Greek companies demonstrate a fairly
satisfactory degree of compliance with corporate governance principles. Moreover this result
became after Hellenic Capital Market Commission’s rules and regulations, so as to enhance
investor protection, Capital Market’s liquidity, trading efficiency, market making and short
selling.
Therefore, corporate governance inside companies’ operation gives them the opportunity to grow
up their financial value and draw capital from the Stock Exchange markets. Lasfer et al. (2003),
report that positive news leads to positive CAR’s and opposite.
III. Methodology
III.1 Event Study Analysis
The events tested for possible abnormal returns are announcements based on management
change, the collaborations with other companies and stock repurchase. According to the
particular methodology, the expected normal stock returns during a period of [t0 ± ti ] days are
examined in combination with the announcement date(t0 ) . The difference between the real and
forecasted returns represents the abnormal stock returns.
The abnormal returns are calculated as the difference between the real and the expected returns
at the duration of [ , ] 1 2 t t days before and after the event’s announcement date ( ) 0 t that we
examine, according to equation (1):
ARit = Rit - RFit (1)
where: = ARit the abnormal attribution of stock i the day t
= Rit the real attribution of stock i.
= RFit the expected attribution of stock i.
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695
We suppose that ARit ~ [0,VAR(ARit )] with VAR(ARit ) 2i e ˜s in case the estimation period is
big. Thus, the statistical importance of abnormal returns can be checked via an estimation of
standardised abnormal return SARit that is determined by equation (2):
( it )
it
it S AR
SAR = AR (2)
Similarly, we can check the cumulative abnormal returns’ AARt statistical importance via
equation (3):
( ) it
t
it S AAR
SAAR = AAR (3)
where: S=
=
N
i
t ARit
N
AAR
1
1 and ( ) = S AARt standard deviation of AARt .
It is realised that, the abnormal returns should be calculated accumulatively for a period [ , ] 1 2 t t
of days for each event, according to equation (4):
S=
=
2
1
,[ 1 , 2 ]
t
t t
i t t it CAR AR (4)
In order to check the cumulative abnormal returns’ statistical importance, we use the following
equation (5):
( ) [ , ]
[ , ]
[ , ]
1 2
1 2
1 2
t t
t t
t t S CAR
CAR
SCAR = (5)
As in the case of AAR's , we can check the CAAR's statistical importance by using equation (6):
( ) [ . ]
[ , ]
[ , ]
1 2
1 2
1 2
t t
t t
t t S CAAR
CAAR
SCAAR = (6)
where: S=
=
N
i
t t CARi t t
N
CAAR
1
[ 1, 2 ] [ 1, 2 ]
1 and ( ) S CAAR[t1 ,t2 ] = Standard deviation of CAAR[t1,t2 ]
In any case we used the t-student distribution (significance level: 5% and 10%). Usually, the
CAR's calculation’s period is between 10 and 50 days before and after the announcement date.
In this study, we used a period of [- 20, +20], that is to say 20 days before and 20 days after the
announcement date ( ) 0 t , while the intermediary time periods are used in order to ratify the
results.
III.2. Bootstrap Methodology
The bootstrap method is a computer-based resampling procedure introduced by Efron (1979)
which has been discussed in the statistics and econometrics literature over the past 20 years (e.g.,
Efron, 1987; Freedman & Peters, 1984a; Freedman & Peters, 1984b and Veall, 1992). When we
are not able to obtain sampling results, we use bootstrap method. This method requires no
analytical calculations. The procedure uses only the original data for resampling to access the
unobservable sampling distribution and to provide a measure of sampling variability, bias, and
confidence intervals. Efron and Tibshirani (1986) propose that the use of the bootstrap:
1. enlarges the type of statistical problem that can be analysed,
2. reduces the assumptions required to validate the analyses, and
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696
3. Eliminates the tedious theoretical calculations associated with the assessment of
accuracy.
In our study, we use bootstrap methodology in order to provide more accurate results. Bootstrap
methodology takes into account arbitrary trend and volatility functions which are useful
exploratory analytical tools. Hence, using this methodology, we are able to provide some insight
into the structure of these functions independent of specific model assumptions.
Our first step is to derive the confidence intervals that will help us to determine the bootstrap
standard error. Then, the original data set t X is bootstrapped to produce a new data set *
t X
having identical or similar time series properties and the original specification is then reestimated
using the new data. This can be repeated N times. A similar procedure has been
discussed from Breiman, 1996, 1999 among others.
A priori, it is not clear if bootstrap methods will work, as we know that standard bootstrap
techniques fail in the context of non-stationary autoregressions, see Basawa et al. (1991). In our
study, we estimate the model with OLS obtaining the residuals. Then, we run the bootstrap
sample 1,000 times and calculate the OLS estimates for model parameters.
To calculate the t-test we follow Krammer (2001):
S=
=
T
i
k i
T
Z t
1
(7)
where ti is the t-statistic of the ?i parameter of the following univariate model:
Ri,t = ai + ßiRm,t + ?iDi + ei,t (8)
where Dt is a dummy variable that takes the value of 1 in the event day and 0 otherwise.
The standard deviation of t is:
1
( )
1
2
^
-
-
=
S=
T
ti t
T
i
s T (9)
Hence we obtain asymptotic normality and following equation (7) we can measure accurately the
effect of the corporate event on the stock price movement.
When the percentage of t*’s are greater than the corresponding statistic calculated based on the
sample which give us the bootstrap, we reach the significance level with a strong p – value and
we accept the null hypothesis that =S
-
t ti /T .
The b draws of t*’s constitute a sample from the sampling distribution of the test statistic under
the null. As b approaches infinity, the empirical distribution constructed based on t*’s will
converge to the true sampling distribution of the statistic under consideration. Hence, the p-value
can be approximated by the percentage of t*’s that are greater than the corresponding statistic
calculated that is based on the sample.
IV. Data
This paper begins with suitable criteria’s selection that is immediately connected with the
objective of our research. In this case the criteria that we used, so as to check if corporate events
influence stock returns, are the following: management change, stock repurchase and
collaborations with other companies. The announcement date is considered to be the official
publication of the above news to all the market and the investors. Data’s sources are the financial
press, as well as firms’ web pages and finally the Athens Stock Exchange.
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697
Firms selected belong to eight different sectors with different capitalization. Succinctly, the
examined sectors are the following: Banks, Information technology, Wholesale Trade,
Insurances, Investment Companies, Non Metal Mining, Constructors and Adviser Companies.
The companies that selected per sector are five so that they constitute, as long as this is possible,
a representative sample of their sector.
Firms’ selection became taking into consideration their sectoral differentiation, so as to get a
better view for the market that does not only represent a sector, which can be influenced
individually from accidental factors. Our sample is constituted by forty enterprises that are
ASE’s members. Finally, present study’s data start from 01st January 2000 and reach up to 31st
December 2006. The examined period includes 150 daily observations (close prices per day),
while event windows include 40 observations, 20 before the company event and 20 after. In
addition, the company events are 97 and conclusions expected to be useful.
V. Empirical results
There are three models to calculate the companies’ abnormal returns: a. Market model, b. Mean
adjusted return model and c. Market adjusted return model. The abnormal returns are the
difference between the real returns and the forecasted returns. The cumulative abnormal returns
concern a better observation of repercussions at the stock prices returns.
Table II presents the cumulative abnormal returns after collaborations’ announcement and the
significant results outline that this kind of announcements affects positively enterprises’ stock
returns. This is clearly observed in the sectors like banking, information technology, insurance,
wholesale trade, non mining and advising sectors, which perform positive event windows after
the announcement. Even though there are many negative CAR’s before the announcement, the
climate is getting positive after the news and results are significant.
Please Insert Table II about here
First of all, banks have positive CAR’s at the event window [- 2, 2], while the event windows [-
5, -1] and [0, 4] are negative, respectively. This means that the announcements for collaborations
in the banking sector are known before they are officially announced but they also do not have
important influence in the CAR’s. Information technology and investment companies have
similar results. Wholesale Trade and Insurance sector seem to be influenced positively by these
announcements. As we can see from Table II, event windows [0, 4], [5, 9] are positive and
statistically significant, when event window [0, 2] is negative. This is a conclusion that refers to
the average CAR’s, too.
Furthermore, the announcements related to collaborations give negative ACAR’s, even if
previous event windows are positive. Sectors like constructors and investment companies seem
to be negatively influenced by collaborations. Negative CAR’s indicate this attitude, but in
general the results outline a different side of this opinion. Most of the investigated sectors have
positive CAR’s after the announcement date, which means that collaboration’s effect is positive
in enterprises’ financial value and positive abnormal returns should follow the collaborations’
announcements.
Table III refers to the management change. Management change is a tool that helps companies to
increase their financial value. This is clearly seen at the average CAR’s column, where event
windows [0, 4] and [5, 9] have bigger positive CAR’s than previous event windows.
Management change creates financial value and helps firms to avoid crises mainly to the
following sectors: information technology, constructors, insurance, non mining & cements.
Please Insert Table III about here
International Research Journal of Applied Finance ISSN 2229 – 6891
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698
Most of the results with positive CAR’s are significant at a level of 5% or 10%. On the other
hand, the negative event windows like the one in wholesale trade sector, the investment
companies and the banking sector are not significant. The strength of the banking sector is the
main reason that we do not have a great impact after a change in management.
Table IV contains the event windows before and after a stock repurchase’s announcement. In this
case, firms’ financial value is expected to increase and the high number of the positive CAR’s
after the announcement date confirms this expectation. Also, before the announcement, there are
more negative observations. Even though there are three negative CAR’s at the event window [-
2, 2], the climate to the next event window is getting better and there is only one negative CAR.
The following windows [5, 9], [10, 14] and [15, 19] have only eight negative CAR’s in total.
Please Insert Table IV about here
The average CAR’s give a clear view of the results. In the event windows before the
announcement – [-20, -16], [-10, -6] and [-5, -1] – the negative CAR’s are significant at the level
of 5%. Following event windows have positive CAR’s which are significant at the level of 10%.
Stock repurchases change the climate and give enterprises the chance to increase their financial
value. Finally, stock repurchases’ impact is statistically important.
Moreover, in order to draw more safe results, study retested the results with bootstrap
methodology.
Please Insert TABLE V, VI, VII, VIII, IX, X, XI about here
The Bootstrap methodology results supports the above findings in short differences and gives a
clear view of the announcements’ impacts.
VI. Conclusions
This study uses the event study analysis methodology and the bootstrap methodology in order to
examine the corporate events announcements’ impacts on stock returns. Despite that fact that all
the aforementioned events should influence the stock returns positively, sometimes – e.g. after a
management change – the impact on stock returns is negative. The results indicate that
collaborations between companies give a negative impact (-0,000839) around the event
announcement [- 2, 2], which is followed by positive observations at the next two event windows
([0,4] and [5,9]). These changes effect, also, the stability on stock’s variability. On the other
hand, examining the behaviour of stock returns after management change, the results outline a
high degree for the event window [0,4]. Generally, there are positive CAR’s in event windows
before and after the announcement. Finally, in the stock repurchase case, the negative and low
degree CAR’s before the announcement are followed by positive and high degree CAR’s.
In conclusion, findings constitute a useful tool for the rational investor, who tries to avoid the
market danger. Even though results are important, corporate governance constitutes a developing
aspect for firms, while management change and collaborations help them to survive during
crucial economic periods. Corporate governance is the new way for companies to follow new
challenges and survive in volatile markets like the turbulent period after the year 2007.
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International Research Journal of Applied Finance ISSN 2229 – 6891
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Appendix
Company Corporate Event
Collaboration Management Change Stock Repurchase
EFG Eurobank 19/4/2005 31/1/2005 23/9/2005
Geniki Bank 17/1/2002 16/5/2003 12/6/2003
Piraeus Bank 19/5/2003 9/8/2001 15/4/2005
Emporiki Bank 15/12/2004 3/12/2004 4/6/2003
Alpha Bank 3/2/2005 22/12/2004 5/8/2004
Pouliadis 1/7/2005 1/7/2004 28/6/2002
Info-Quest 29/9/2005 28/6/2001 -
Informatics 26/10/2004 30/6/2004 26/7/2004
PC Systems 13/6/2005 10/8/2005 13/7/2004
CPI 19/7/2004 25/9/2001 14/2/2003
Themeliodomi 1/8/2003 4/7/2003 28/11/2003
Aktor 2/5/2001 26/9/2005 -
Aegek - 2/7/2003 29/6/2004
Diekat - 7/7/2004 29/6/2004
Proodeftiki 21/9/2004 12/5/2003 -
Dionic 15/6/2005 1/7/2005 24/4/2003
Druckfarben 28/11/2002 2/7/2004 7/7/2003
Benroubi 27/7/2005 29/7/2005 1/6/2005
Mytilineos 11/5/2004 25/6/2003 -
Keranis 7/5/2001 - 14/7/2004
Lamda Development 9/1/2003 2/6/2005 20/3/2003
Notos 8/7/2003 26/5/2004 11/6/2004
Boutaris 7/3/2002 7/7/2003 28/12/2004
Fourlis 4/12/2001 9/3/2005 -
Delta Holdings 23/11/2001 - 3/4/2001
Aspis Pronoia 10/2/2004 29/1/2004 -
Evropaiki Pisti 12/3/2003 4/1/2005 24/5/2005
Phoenix 8/4/2003 30/6/2005 -
Ethniki Asfalistiki - 5/8/2004 -
Agrotiki Asfalistiki - 20/10/2003 29/9/2003
Optima 8/10/2004 - 29/6/2004
Eoliki - 8/2/2005 26/7/2005
Active - 11/7/2002 -
Ellinikes Ependiseis - 24/3/2005 -
Proodos 18/4/2001 14/4/2005 2/6/2005
?itan 26/6/2001 24/5/2004 24/1/2005
?etanet 27/9/2004 - 2/9/2004
Mathios Pyrimaha - 28/7/2005 28/7/2005
Table I
Sample
?ktinos 25/5/2005 3/6/2003 29/12/2003
?yriakidis Marbles - 14/3/2003 8/3/2004
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Event
Windo
ws
Banks Informati
on
Technolo
gy
Construct
ors
Wholesal
e Trade
Advising
Compani
es
Insurance Investme
nt
Non
Mining
&
Cements
Average
[-20,-
16]
-
0,000732
0,036317 0,009368 0,041619 0,008285 -0,00729 -
0,01043*
*
-
0,01354
**
0,008031975
[-15,-
11]
0,028952 -
0,12185*
-0,01255 0,021039 0,039276 0,008759 0,01418 -
0,02896
*
-
0,00639425*
*
[-10,-6]
0,004611
4*
0,081025 0,010874 0,018249 0,0169** -
0,01322*
*
0,015891 0,00811
6
0,0178058
[-5,-1]
-
0,019084
**
0,059568 -0,01413* -
0,00676*
0,029503 0,023994 -
0,00604*
-
0,01144
**
0,006951375
*
[-2,2]
0,003794
*
0,027144
**
-0,00652 -
0,00211*
*
-0,0133 -0,02262 0,006692 0,00020
3*
-
0,000839625
[0,4]
-
0,016823
-0,02003 -0,01671 0,045698
**
-0,01574 0,039586
*
-0,00476 0,02646
**
0,004710125
**
[5,9]
0,005381
4
0,016592
*
-0,01981 0,019843
*
0,004797
*
0,060851
**
0,012549
*
-0,01275 0,010931675
*
[10,14]
-
0,005595
0,094617
**
0,009035*
*
-0,03282 -0,0517 0,017475
**
-0,03547 -0,01633 -0,0025985
[15,19]
0,026908
**
-0,03714 0,016809 0,004337 0,017611 0,00148 -
0,00109*
*
0,04319
4
0,009013625
**
Table II
Collaborati
ons –
CAR’s * 5% Significance Level, ** 10% Significance Level.
Event
Windo
ws
Banks Informati
on
Technolo
gy
Constructo
rs
Wholesale
Trade
Advising
Companie
s
Insuranc
e
Investmen
t
Non
Mining
&
Cements
Average
[-20,-
16]
0,002412 -0,02162 0,029398 -
0,02635*
*
-
0,00511*
*
0,00884
3
-0,0297** -
0,02565*
-
0,00847*
*
[-15,- 0,03645 0,017623 - -0,00339* 0,007338 - 0,007078 0,032261 0,008185
International Research Journal of Applied Finance ISSN 2229 – 6891
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703
11] 0,00074** 0,03114
[-10,-6]
-
0,04593*
-0,05862* 0,038213 0,045803 0,009793 0,02387
1
0,008061 0,004688 0,003235
[-5,-1]
0,003238 -
0,07382**
0,006805 0,081066 0,032163 -
0,03696
-0,00989* 0,002878 0,000685
[-2,2]
-
0,00234*
*
0,001142*
*
0,000289* -0,01942 0,018505
**
0,02244
3
0,006193
*
-
0,00314*
*
0,002959
*
[0,4]
0,004833
*
0,045001* 0,04771* -0,00502 0,019825
**
0,03802
8
-0,00363 0,022778
*
0,021191
**
[5,9]
-0,00247 0,03407** -0,01743 -0,0035 0,002223
*
0,08293
7
0,023663
**
0,044472
*
0,020496
**
[10,14]
0,005282 -0,05009 0,002886*
*
0,031124 -0,00581 -
0,03648
-0,00478 0,009304 -0,00607
[15,19]
0,030645 0,14398* 0,056436*
*
0,041202
**
0,007353
*
0,01925 -0,00338 -0,03557 0,03249*
*
Table III
Manageme
nt Change–
CAR’s * 5% Significance Level, ** 10% Significance Level.
Event
Windo
ws
Banks Informati
on
Technolo
gy
Constructo
rs
Wholesal
e Trade
Advising
Companie
s
Insurance Investmen
t
Non
Mining &
Cements
Average
[-20,-
16]
-0,02566 -0,02277* 0,012898 0,004588 0,00101 -0,01285 0,01284 0,007648 -0,00279
[-15,-
11]
0,003571
*
0,008491 -
0,03103**
0,026748
**
0,016028 -0,00378 0,008024 0,02234 0,006299
[-10,-6]
0,007655 -
0,04648*
*
-0,01547* 0,003216 -0,02121 -
0,00449*
*
-
0,01287*
*
0,000899 -0,01109*
[-5,-1]
0,006566 -
0,00632*
*
-
0,02616**
0,002259
**
-0,00504* -
0,00731*
*
-
0,01601*
*
0,013791 -
0,00478*
*
[-2,2]
0,002838
**
0,017549
*
-0,0017 0,002312
*
-0,00945 0,005769
*
0,002868 -0,00621 0,001747
**
[0,4]
0,004327
*
0,053423
**
0,011674*
*
0,044571
*
0,016735
*
-0,01122 0,033478
*
0,009539
*
0,020316
*
[5,9]
0,023472
**
-0,0273 0,001213* 0,028168
**
0,006033
**
0,000509
**
0,004294
**
-0,02729 0,001137
*
[10,14]
0,003487
*
-0,01086 -0,03623 0,025582 0,014356
*
0,009413 0,019554
*
0,006642
**
0,003993
**
[15,19]
-0,00915 0,050176 0,026841*
*
-0,00712 -0,00671 0,050653
*
0,004581 -0,00913 0,012518
Table * 5% Significance Level, ** 10% Significance Level.
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704
IV
Stock
repurcha
se –
CAR’s
Day Collaborations Management Change Stock Repurchase
Abnormal Returns Bootstrap Abnormal Bootstrap Abnormal Bootstrap
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705
(ARs) Results (ARs) Returns
(ARs)
Results (ARs) Returns
(ARs)
Results (ARs)
-20 0,014715 0,015415 0,011221 0,011921 -0,00241 -0,00171
-19 0,00129 0,00177 0,004196 0,004676 -0,01132 -0,01084
-18 -0,01138 -0,005045 -0,01346 -0,004632 0,000624 -0,005348
-17 -0,0071 -0,0053603 -0,00234 -0,004333 -0,01336 -0,003977
-16 0,002399 0,003099 0,002801 0,003501 0,000805 0,001505
-15 0,007143 0,007623 0,009079 0,009559 -0,00064 -0,00016
-14 0,007755 0,007449 0,003763 0,006421 0,001032 0,000196
-13 -0,00635 0,0036383 0,001844 0,007404 0,008674 -0,0008747
-12 0,00951 0,01021 0,016605 0,017305 -0,01233 -0,01163
-11 0,010895 0,011375 0,005158 0,005638 0,006833 0,007313
-10 0,007575 0,009235 -0,00984 -0,002341 -0,00233 0,0022515
-9 0,001764 0,0046263 -0,03429 -0,0168167 -0,01064 -0,002832
-8 0,00454 0,00524 -0,00632 -0,00562 0,004474 0,005174
-7 -0,00577 -0,00529 0,006725 0,007205 0,014836 0,015316
-6 -0,00349 -0,00463 -0,00221 0,0022575 0,001315 0,0080755
-5 -0,00368 -0,001349 -0,00485 0,0025137 -0,00239 -0,0032283
-4 0,003123 0,003823 0,014601 0,015301 -0,00861 -0,00791
-3 -0,00191 -0,00143 0,000008 0,000488 -0,02044 -0,01996
-2 -0,00976 -0,005835 -0,00472 -0,002356 0,029837 0,0046985
-1 -0,00685 -0,004272 -0,0018 -0,0029533 0,008166 0,0136137
0 0,003794 0,004494 -0,00234 -0,00164 0,002838 0,003538
1 -0,00166 -0,00118 0,000919 0,001399 -0,01153 -0,01105
2 -0,00738 -0,00452 -0,00726 -0,0031705 0,013822 0,001146
3 0,000317 -0,0063177 0,006928 0,0020873 0,000822 0,004338
4 -0,01189 -0,01119 0,006594 0,007294 -0,00163 -0,00093
5 -0,00618 -0,0057 -0,0068 -0,00632 0,003904 0,004384
6 -0,0026 -0,00439 0,003357 -0,0017215 0,020725 0,0123145
7 0,006704 0,003867 -0,00808 -0,0009277 -0,01126 0,00647
8 0,007497 0,008197 0,00194 0,00264 0,009945 0,010645
9 -0,00004 0,00044 0,007119 0,007599 0,000152 0,000632
10 0,013024 0,006492 -0,00631 0,0004045 -0,02209 -0,010969
11 -0,00162 0,0051683 0,008683 0,0041073 -0,0074 -0,0019163
12 0,004101 0,004801 0,009949 0,010649 0,023741 0,024441
13 -0,03497 -0,03449 -0,000016 0,000464 0,007055 0,007535
14 0,013871 -0,0105495 -0,00703 -0,003523 0,002183 0,004619
15 0,007519 0,010336 0,003114 -0,002132 -0,00187 -0,002619
16 0,009618 0,010318 -0,00248 -0,00178 -0,00817 -0,00747
17 0,013388 0,013868 0,007887 0,008367 0,000153 0,000633
Table V 18 0,005851 0,0096195 0,019487 0,013687 -0,00428 -0,0020635
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706
Banks 19 -0,00947 -0,0018095 0,002641 0,011064 0,005017 0,0003685
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Vol – II Issue – 6 June, 2011
707
Day Collaborations Management Change Stock Repurchase
Abnormal
Returns
(ARs)
Bootstrap
Results (ARs)
Abnormal
Returns
(ARs)
Bootstrap Abnormal
Returns
(ARs)
Bootstrap
Results
(ARs)
Results
(ARs)
-20 0,023001 0,023701 -0,00363 -0,00293 -0,02769 -0,02699
-19 -0,00099 -0,00051 -0,00188 -0,0014 -0,05852 -0,05804
-18 0,005141 0,0020755 -0,01332 -0,0076 -0,00819 -0,033355
-17 0,006555 0,00477 0,002898 -0,005364 0,036246 0,0211453
-16 0,002614 0,003314 -0,00567 -0,00497 0,03538 0,03608
-15 0,007545 0,008025 0,049916 0,050396 0,016743 0,017223
-14 -0,00893 -0,000692 0,024705 0,0373105 -0,01638 0,0001815
-13 -0,03186 -0,03046 -0,01858 0,002943 0,001884 -0,004343
-12 -0,05059 -0,04989 0,002704 0,003404 0,001465 0,002165
-11 -0,03802 -0,03754 -0,04112 -0,04064 0,004783 0,005263
-10 0,080299 0,0211395 -0,00092 -0,02102 -0,00568 -0,000448
-9 -0,00732 0,016743 -0,02614 -0,002605 -0,01244 -0,00891
-8 -0,02275 -0,02205 0,019245 0,019945 -0,00861 -0,00791
-7 0,002354 0,002834 -0,02257 -0,02209 -0,01394 -0,01346
-6 0,028441 0,0153975 -0,02823 -0,0254 -0,00581 -0,009875
-5 0,017764 0,0238043 0,029018 -0,00528 -0,00446 -0,011543
-4 0,025208 0,025908 -0,01663 -0,01593 -0,02436 -0,02366
-3 0,002592 0,003072 -0,03104 -0,03056 0,023397 0,023877
-2 -0,00095 0,000821 -0,01713 -0,024085 -0,01076 0,0063185
-1 0,014952 0,0137153 -0,03803 -0,018006 0,009854 0,0055477
0 0,027144 0,027844 0,001142 0,001842 0,017549 0,018249
1 -0,03564 -0,03516 0,029435 0,029915 0,020789 0,021269
2 -0,03658 -0,03611 0,032512 0,0309735 0,008436 0,0146125
3 0,03925 -0,003843 -0,00083 0,0048073 -0,00606 0,0050283
4 -0,0142 -0,0135 -0,01726 -0,01656 0,012709 0,013409
5 -0,00064 -0,00016 0,006534 0,007014 -0,01779 -0,01731
6 0,01299 0,006175 0,006106 0,00632 0,002356 -0,007717
7 -0,00973 0,0064537 0,012944 0,007838 -0,03216 -0,008259
8 0,016101 0,016801 0,004464 0,005164 0,005027 0,005727
9 -0,00213 -0,00165 0,004022 0,004502 0,015265 0,015745
10 -0,01598 -0,009055 0,006422 0,005222 0,01137 0,0133175
11 0,033652 0,0156977 0,001414 -0,002144 -0,00248 -0,00244
12 0,029421 0,030121 -0,01427 -0,01357 -0,01621 -0,01551
13 0,014149 0,014629 -0,02959 -0,02911 0,004015 0,004495
14 0,033371 0,02376 -0,01407 -0,02183 -0,00756 -0,001772
15 -0,02779 0,0076483 0,01988 0,0054887 0,005635 0,001793
16 0,017364 0,018064 0,010656 0,011356 0,007304 0,008004
Table VI 17 -0,00577 -0,00529 0,015356 0,015836 0,004841 0,005321
Information 18 -0,00715 -0,00646 0,056235 0,0357955 0,018165 0,011503
Technology 19 -0,01379 -0,01047 0,041852 0,0490435 0,014231 0,016198
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
708
Day Collaborations Management Change Stock Repurchase
Abnormal
Returns (ARs)
Bootstrap
Abnormal
Returns (ARs)
Bootstrap
Abnormal
Returns (ARs)
Bootstrap
Results (ARs) Results (ARs) Results (ARs)
-20 -0,01106 -0,01036 -0,00328 -0,00258 0,02526 0,02596
-19 0,010192 0,010672 0,01637 0,01685 0,006635 0,007115
-18 -0,00528 0,002456 -0,00316 0,006605 0,00399 0,0053125
-17 0,007539 0,003412 0,00517 0,0054353 -0,00999 -0,00633
-16 0,007977 0,008677 0,014296 0,014996 -0,01299 -0,01229
-15 -0,01154 -0,01106 0,005246 0,005726 0,004784 0,005264
-14 0,004582 -0,003479 -0,00423 0,000508 -0,0081 -0,001658
-13 0,00301 0,0015307 0,002552 -0,003486 -0,00425 -0,00795
-12 -0,003 -0,0023 -0,00878 -0,00808 -0,0115 -0,0108
-11 -0,0056 -0,00512 0,004474 0,004954 -0,01197 -0,01149
-10 -0,00089 -0,003245 0,02685 0,015662 0,005362 -0,003304
-9 0,010119 0,0003097 -0,02412 -0,0016033 -0,00812 -0,0043693
-8 -0,0083 -0,0076 -0,00754 -0,00684 -0,01035 -0,00965
-7 0,009894 0,010374 0,040565 0,041045 0,009114 0,009594
-6 0,000048 0,004971 0,002455 0,02151 -0,01147 -0,001178
-5 -0,00955 -0,00211 0,022908 0,0039977 -0,00258 -0,0056067
-4 0,003172 0,003872 -0,01337 -0,01267 -0,00277 -0,00207
-3 -0,00547 -0,00499 -0,00995 -0,00947 -0,01498 -0,0145
-2 0,000978 -0,002246 0,00059 -0,00468 -0,02544 -0,02021
-1 -0,00326 -0,002934 0,006631 0,0025033 0,019611 -0,0025097
0 -0,00652 -0,00582 0,000289 0,000989 -0,0017 -0,001
1 -0,00701 -0,00653 0,010643 0,011123 0,008932 0,009412
2 -0,000009 -0,0035095 0,004826 0,0077345 -0,00459 0,002171
3 -0,01018 -0,0010627 0,023541 0,0122593 0,009333 0,001481
4 0,007001 0,007701 0,008411 0,009111 -0,0003 0,0004
5 0,001028 0,001508 -0,0177 -0,01722 -0,00478 -0,0043
6 -0,00195 -0,000461 0,013729 -0,0019855 -0,000073 -0,0024265
7 -0,01366 -0,00679 -0,02424 0,0024723 0,000004 0,0022213
8 -0,00476 -0,00406 0,017928 0,018628 0,006733 0,007433
9 -0,00047 0,00001 -0,00716 -0,00668 -0,00067 -0,00019
10 0,011849 0,0056895 -0,0082 -0,00768 0,004501 0,0019155
11 -0,00165 0,0031663 -0,01408 -0,0006167 -0,01455 -0,0077597
12 -0,0007 0 0,02043 0,02113 -0,01323 -0,01253
13 0,003552 0,004032 0,00242 0,0029 -0,00974 -0,00926
14 -0,00402 -0,000234 0,002316 0,002368 -0,00322 -0,00648
15 -0,00681 0,0080717 0,009635 0,0059273 0,006899 0,0026037
16 0,035045 0,035745 0,005831 0,006531 0,004132 0,004832
17 0,004613 0,005093 -0,00082 -0,00034 -0,01766 -0,01718
Table VII 18 -0,00592 -0,0006535 0,007591 0,0033855 0,016111 -0,0007745
Constructors 19 -0,01012 -0,00802 0,034196 0,0208935 0,017363 0,016737
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
709
Day Collaborations Management Change Stock Repurchase
Abnormal
Returns (ARs)
Bootstrap Abnormal Bootstrap Abnormal Bootstrap
Results (ARs) Returns (ARs) Results (ARs) Returns (ARs) Results (ARs)
-20 0,01134 0,01204 -0,00423 -0,00353 0,002418 0,003118
-19 0,008246 0,008726 -0,00449 -0,00401 -0,00113 -0,00065
-18 0,023519 0,0158825 -0,01852 -0,011505 0,004399 0,0016345
-17 -0,00101 0,0073463 0,010759 -0,005877 0,026848 0,0011023
-16 -0,00047 0,00023 -0,00987 -0,00917 -0,02794 -0,02724
-15 0,000016 0,000496 -0,00074 -0,00026 -0,01868 -0,0182
-14 0,014457 0,0072365 0,010083 0,0046715 0,025278 0,003299
-13 0,002135 0,008643 -0,00881 0,001411 0,005257 0,008255
-12 0,009337 0,010037 0,00296 0,00366 -0,00577 -0,00507
-11 -0,00491 -0,00443 -0,00688 -0,0064 0,020668 0,021148
-10 0,008232 0,001661 0,030986 0,012053 -0,01913 0,000769
-9 0,001662 0,003701 -0,0014 0,0071987 0,008095 -0,0019073
-8 0,001209 0,001909 -0,00799 -0,00729 0,005313 0,006013
-7 0,004455 0,004935 0,011244 0,011724 0,008436 0,008916
-6 0,002691 0,003573 0,012964 0,012104 0,000504 0,00447
-5 -0,00735 -0,006133 -0,0068 0,019762 -0,00738 -0,0074287
-4 -0,01374 -0,01304 0,053122 0,053822 -0,01541 -0,01471
-3 0,014787 0,015267 0,020698 0,021178 -0,00063 -0,00015
-2 0,020077 0,017432 -0,00317 0,008764 0,012954 0,006162
-1 -0,02053 -0,0008543 0,017218 -0,0017907 0,012719 0,0093283
0 -0,00211 -0,00141 -0,01942 -0,01872 0,002312 0,003012
1 0,02763 0,02811 -0,00451 -0,00403 0,012193 0,012673
2 0,028105 0,0278675 0,00205 -0,00123 0,013396 0,0127945
3 -0,01618 0,006727 0,004193 0,006304 0,022835 0,0100237
4 0,008256 0,008956 0,012669 0,013369 -0,00616 -0,00546
5 0,002708 0,003188 -0,02375 -0,02327 0,008483 0,008963
6 -0,00707 -0,002181 0,00857 -0,00759 -0,0009 0,0037915
7 -0,01012 -0,0045033 0,015607 0,012603 0,007243 0,0028033
8 0,00368 0,00438 0,013632 0,014332 0,002067 0,002767
9 0,030645 0,031125 -0,01757 -0,01709 0,011278 0,011758
10 0,004517 0,017581 0,020437 0,0014335 0,009871 0,0105745
11 -0,02477 -0,0076177 0,000629 0,0125107 0,00567 0,0066833
12 -0,0026 -0,0019 0,016466 0,017166 0,004509 0,005209
13 -0,00028 0,0002 -0,00051 -0,00003 0,002693 0,003173
14 -0,00969 -0,004985 -0,0059 -0,003205 0,00284 0,0027665
15 0,006875 -0,004695 0,030104 0,0186227 0,00679 0,008838
16 -0,01127 -0,01057 0,031664 0,032364 0,016884 0,017584
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
710
Table VIII 17 -0,01083 -0,01035 -0,01371 -0,01323 -0,00109 -0,00061
Wholesale 18 0,018501 0,0038355 -0,01125 -0,01248 -0,03143 -0,01626
Trade 19 0,001055 0,009778 0,004396 -0,003427 0,001723 0,012387
Day Collaborations Management Change Stock Repurchase
Abnormal
Returns (ARs)
Bootstrap Abnormal Bootstrap Abnormal
Returns
(ARs)
Bootstrap
Results (ARs) Returns (ARs) Results (ARs) Results (ARs)
-20 -0,01226 -0,01156 0,009451 0,010151 -0,00543 -0,00473
-19 0,003425 0,003905 -0,01848 -0,018 0,004083 0,004563
-18 0,009443 0,006434 0,00418 -0,00715 0,012402 0,0082425
-17 0,007932 0,0057083 -0,0007 0,0013083 -0,00449 0,0007873
-16 -0,00025 0,00045 0,000445 0,001145 -0,00555 -0,00485
-15 0,012517 0,012997 -0,00508 -0,0046 -0,00752 -0,00704
-14 0,025972 0,0192445 0,009659 0,0022895 -0,00599 -0,006755
-13 0,000663 0,0074917 0,020365 0,002838 0,024507 0,0097067
-12 -0,00416 -0,00346 -0,02151 -0,02081 0,010603 0,011303
-11 0,004282 0,004762 0,003904 0,004384 -0,00557 -0,00509
-10 0,004164 0,004223 -0,00887 -0,002483 -0,01427 -0,00992
-9 -0,00485 0,0101113 0,003718 -0,0012357 -0,00712 -0,0058633
-8 0,03102 0,03172 0,001445 0,002145 0,0038 0,0045
-7 -0,006 -0,00552 0,007214 0,007694 0,001945 0,002425
-6 -0,00743 -0,006715 0,006282 0,006748 -0,00556 -0,0018075
-5 0,01219 0,008558 -0,00421 -0,002686 -0,00296 -0,0038367
-4 0,020914 0,021614 -0,01013 -0,00943 -0,00299 -0,00229
-3 0,006562 0,007042 0,030261 0,030741 -0,01522 -0,01474
-2 0,003192 0,004877 -0,00105 0,0146055 0,014071 -0,0005745
-1 -0,01335 -0,0078193 0,017302 0,0115857 0,002051 0,002224
0 -0,0133 -0,0126 0,018505 0,019205 -0,00945 -0,00875
1 0,012179 0,012659 0,002783 0,003263 0,01191 0,01239
2 0,003422 0,0078005 0,020079 0,011431 0,017259 0,0145845
3 -0,01929 -0,0048737 -0,00668 -0,0004903 0,001281 0,0047567
4 0,001247 0,001947 -0,01487 -0,01417 -0,00427 -0,00357
5 0,020017 0,020497 0,004589 0,005069 -0,00014 0,00034
6 0,008372 0,0141945 -0,0124 -0,0039055 -0,00091 -0,000525
7 -0,01728 -0,0059327 -0,00082 -0,000544 -0,0114 -0,0020237
8 -0,00889 -0,00819 0,011588 0,012288 0,006239 0,006939
9 0,002577 0,003057 -0,00074 -0,00026 0,012248 0,012728
10 -0,00014 0,0012185 0,010368 0,004814 -0,00394 0,004154
11 -0,00734 -0,0038667 0,000975 0,0001343 -0,00145 0,00222
12 -0,00412 -0,00342 -0,01094 -0,01024 0,01205 0,01275
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
711
13 -0,01862 -0,01814 0,000415 0,000895 0,009217 0,009697
14 -0,02149 -0,020055 -0,00663 -0,0031075 -0,00152 0,0038485
15 -0,00425 -0,0097733 -0,01114 -0,003889 -0,00613 -0,0029867
16 -0,00358 -0,00288 0,006103 0,006803 -0,00131 -0,00061
Table IX 17 0,02766 0,02814 0,004514 0,004994 0,002164 0,002644
Advising 18 -0,0041 0,01178 -0,00597 -0,000728 -0,01142 -0,004628
Firms 19 0,001885 -0,0011075 0,013845 0,0039375 0,009981 -0,0007195
Day Collaborations Management Change Stock Repurchase
Abnormal
Returns (ARs)
Bootstrap Abnormal Bootstrap
Abnormal
Returns (ARs)
Bootstrap
Results (ARs)
Returns
(ARs) Results (ARs) Results (ARs)
-20 0,012311 0,013011 -0,00539 -0,00469 -0,000093 0,000607
-19 0,012439 0,012919 0,007892 0,008372 -0,01812 -0,01764
-18 -0,02437 -0,0059655 0,005645 0,0067685 -0,00618 -0,01215
-17 -0,01202 -0,010682 0,003739 0,0021147 0,010201 0,0017877
-16 0,004344 0,005044 -0,00304 -0,00234 0,001342 0,002042
-15 -0,02291 -0,02243 0,006438 0,006918 -0,00769 -0,00721
-14 0,041469 0,0092795 -0,02918 -0,011371 -0,00947 -0,00858
-13 -0,00713 0,005483 -0,01056 -0,0137833 0,01305 0,0025177
-12 -0,01789 -0,01719 -0,00161 -0,00091 0,003973 0,004673
-11 0,01522 0,0157 0,003775 0,004255 -0,00364 -0,00316
-10 -0,03903 -0,011905 -0,00027 0,0017525 0,004136 0,000248
-9 0,007995 -0,005076 -0,00339 0,0025593 -0,00069 0,0013363
-8 0,015807 0,016507 0,011338 0,012038 0,000563 0,001263
-7 0,006473 0,006953 0,007928 0,008408 -0,00208 -0,0016
-6 -0,00447 0,0010015 0,00826 0,008094 -0,00641 -0,004245
-5 -0,0231 -0,0062723 -0,01811 -0,0012677 0,001316 -0,002348
-4 0,008753 0,009453 0,006047 0,006747 -0,00195 -0,00125
-3 0,022499 0,022979 -0,00469 -0,00421 0,002588 0,003068
-2 0,011674 0,0170865 -0,00919 -0,00694 -0,01379 -0,005601
-1 0,004167 -0,0022597 -0,01102 0,0007443 0,004525 -0,0011653
0 -0,02262 -0,02192 0,022443 0,023143 0,005769 0,006469
1 0,013613 0,014093 0,010152 0,010632 -0,01421 -0,01373
2 0,00944 0,0115265 0,018181 0,0141665 0,004991 -0,0046095
3 0,006291 0,0161977 -0,01123 0,0018137 -0,00314 -0,0009263
4 0,032862 0,033562 -0,00151 -0,00081 -0,00463 -0,00393
5 0,01408 0,01456 0,012419 0,012899 0,009128 0,009608
6 -0,01871 -0,002315 0,022619 0,017519 -0,00663 0,001249
7 0,016901 -0,0040363 0,023262 0,026563 0,004173 0,0005537
8 -0,0103 -0,0096 0,033808 0,034508 0,004118 0,004818
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
712
9 0,058878 0,059358 -0,00917 -0,00869 -0,01028 -0,0098
10 0,01397 0,036424 -0,00604 -0,007605 0,005486 -0,002397
11 -0,01256 7,333E-05 -0,00127 -0,00331 -0,00299 0,004221
12 -0,00119 -0,00049 -0,00262 -0,00192 0,010167 0,010867
13 0,001628 0,002108 -0,0107 -0,01022 -0,00671 -0,00623
14 0,015623 0,0086255 -0,01585 -0,013275 0,003464 -0,001623
15 -0,0115 0,012403 0,016197 0,001148 -0,00607 0,0014663
16 0,033086 0,033786 0,003097 0,003797 0,007005 0,007705
17 -0,01171 -0,01123 -0,01663 -0,01615 0,011358 0,011838
Table X 18 -0,01146 -0,011585 0,016519 -0,0000555 0,027669 0,0195135
Insurance 19 0,003061 -0,0041995 0,000069 0,008294 0,010686 0,0191775
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
713
Day Collaborations Management Change Stock Repurchase
Abnormal
Returns
(ARs)
Bootstrap Abnormal Bootstrap Abnormal
Returns
(ARs)
Bootstrap
Results (ARs) Returns (ARs) Results (ARs) Results (ARs)
-20 -0,00488 -0,00418 -0,0066 -0,0059 0,021881 0,022581
-19 -0,0032 -0,00272 -0,000015 0,000465 0,001262 0,001742
-18 -0,00189 -0,002545 -0,01316 -0,0065875 -0,01302 -0,005879
-17 -0,00071 -0,0007817 0,001204 -0,0076953 0,000234 -0,003436
-16 0,000255 0,000955 -0,01113 -0,01043 0,002478 0,003178
-15 -0,00222 -0,00174 -0,00181 -0,00133 0,00923 0,00971
-14 -0,014 -0,00811 0,000947 -0,0004315 -0,00051 0,00436
-13 0,001513 -0,0035507 0,006676 0,0037597 -0,00408 0,0013743
-12 0,001835 0,002535 0,003656 0,004356 0,008713 0,009413
-11 0,027054 0,027534 -0,00239 -0,00191 -0,00534 -0,00486
-10 -0,01614 0,005457 0,004425 0,0010175 -0,01162 -0,00848
-9 0,021159 0,0006663 -0,00175 -0,000505 -0,00005 -0,003749
-8 -0,00302 -0,00232 -0,00419 -0,00349 0,000423 0,001123
-7 -0,00348 -0,003 -0,00039 0,00009 -0,00496 -0,00448
-6 0,017362 0,006941 0,009966 0,004788 0,003345 -0,0008075
-5 0,00389 0,0005307 -0,00111 0,0047773 0,00969 0,0023217
-4 -0,01966 -0,01896 0,005476 0,006176 -0,00607 -0,00537
-3 -0,00464 -0,00416 -0,00185 -0,00137 -0,01937 -0,01889
-2 0,004971 0,0001655 -0,0183 -0,010075 -0,00126 -0,010315
-1 0,009399 0,0070207 0,005898 -0,0020697 0,001003 0,0008703
0 0,006692 0,007392 0,006193 0,006893 0,002868 0,003568
1 0,00043 0,00091 -0,00598 -0,0055 0,015733 0,016213
2 -0,00054 -0,000055 -0,00274 -0,00436 0,007826 0,0117795
3 -0,01264 -0,0039617 -0,00267 -0,0012813 0,006884 0,004959
4 0,001295 0,001995 0,001566 0,002266 0,000167 0,000867
5 0,004738 0,005218 0,00293 0,00341 0,00163 0,00211
6 -0,00186 0,001439 0,005022 0,003976 0,020534 0,011082
7 -0,00502 0,0029783 -0,01308 -0,0013257 -0,00554 0,0008347
8 0,015815 0,016515 0,004081 0,004781 -0,01249 -0,01179
9 -0,00113 -0,00065 0,024709 0,025189 0,000163 0,000643
10 -0,00807 -0,0046 -0,00422 0,0102445 0,010711 0,005437
11 0,001724 -0,0045353 -0,00651 -0,0007323 0,008345 0,0056853
12 -0,00726 -0,00656 0,008533 0,009233 -0,002 -0,0013
13 -0,00296 -0,00248 -0,00181 -0,00133 -0,00764 -0,00716
14 -0,01891 -0,010935 -0,00078 -0,001295 0,010141 0,0012505
15 -0,01399 -0,0081437 -0,02205 -0,00688 0,007357 0,0049727
16 0,008469 0,009169 0,00219 0,00289 -0,00258 -0,00188
17 -0,00311 -0,00263 0,008753 0,009233 0,008873 0,009353
Table XI 18 0,009183 0,0030365 0,004914 0,0068335 -0,00296 0,0029565
Investment 19 -0,00165 0,0037665 0,002817 0,0038655 -0,00611 -0,004535
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
714
Day Collaborations Management Change Stock Repurchase
Abnormal
Returns
(ARs)
Bootstrap Abnormal Bootstrap
Abnormal
Returns (ARs)
Bootstrap
Results (ARs)
Returns
(ARs) Results (ARs) Results (ARs)
-20 -0,000002 0,000698 -0,00513 -0,00443 -0,0051 -0,0044
-19 -0,00743 -0,00695 -0,00178 -0,0013 -0,00044 0,00004
-18 0,003959 -0,0017355 -0,00886 -0,00532 -0,00412 -0,00228
-17 -0,00551 -0,002037 -0,01373 -0,006246 -0,00303 0,004396
-16 -0,00456 -0,00386 0,003852 0,004552 0,020338 0,021038
-15 -0,01808 -0,0176 0,015882 0,016362 0,004263 0,004743
-14 -0,00103 -0,009555 0,004176 0,010029 -0,00018 0,0020415
-13 -0,00869 -0,0027987 0,008381 0,0069467 0,02484 0,011718
-12 0,001324 0,002024 0,008283 0,008983 0,010494 0,011194
-11 -0,00249 -0,00201 -0,00446 -0,00398 -0,01708 -0,0166
-10 0,004905 0,0012075 0,006581 0,0010605 0,007489 -0,0047955
-9 -0,00202 -0,0011717 0,000949 0,0060983 -0,00559 0,004616
-8 -0,0064 -0,0057 0,010765 0,011465 0,011949 0,012649
-7 0,003076 0,003556 -0,00634 -0,00586 -0,01167 -0,01119
-6 0,008556 0,005816 -0,00727 -0,006805 -0,00127 -0,00647
-5 -0,00798 0,0071603 -0,00789 -0,0005627 -0,00213 0,0065383
-4 0,020905 0,021605 0,013472 0,014172 0,023015 0,023715
-3 -0,0048 -0,00432 0,000864 0,001344 -0,00368 -0,0032
-2 -0,00203 -0,003415 -0,02743 -0,013283 0,000327 -0,0016765
-1 -0,01752 -0,006449 0,023859 -0,002237 -0,00374 -0,0032077
0 0,000203 0,000903 -0,00314 -0,00244 -0,00621 -0,00551
1 0,006667 0,007147 0,002018 0,002498 0,017977 0,018457
2 0,009283 0,007975 0,004294 0,003156 -0,0013 0,0083385
3 0,015742 0,0065283 0,023115 0,0079663 -0,00422 -0,0007447
4 -0,00544 -0,00474 -0,00351 -0,00281 0,003286 0,003986
5 0,007442 0,007922 0,004255 0,004735 -0,01339 -0,01291
6 -0,00049 0,003476 -0,00666 -0,0012025 -0,00462 -0,009005
7 0,00233 -0,0007233 0,012374 0,0114323 -0,00891 -0,0068133
8 -0,00401 -0,00331 0,028583 0,029283 -0,00691 -0,00621
9 -0,01803 -0,01755 0,005915 0,006395 0,006537 0,007017
10 0,003846 -0,007092 -0,01398 -0,0040325 -0,00394 0,0012985
11 -0,00748 0,002511 -0,01294 -0,00959 0,009819 -0,000467
12 0,011167 0,011867 -0,00185 -0,00115 -0,00728 -0,00658
13 -0,01321 -0,01273 0,020822 0,021302 -0,01704 -0,01656
14 -0,01065 -0,01193 0,017259 0,0190405 0,025094 0,004027
15 0,004473 -0,00091 -0,00028 0,0032097 0,021395 0,015687
16 0,003447 0,004147 -0,00735 -0,00665 0,000572 0,001272
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
715
Table XII 17 0,019755 0,020235 -0,02139 -0,02091 -0,02324 -0,02276
Non Mining 18 -0,01108 0,0043375 -0,00545 -0,01342 -0,01151 -0,017375
& Cements 19 0,026596 0,007758 -0,00111 -0,00328 0,003648 -0,003931
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
716
Editorial Board
Editor-In-Chief
Dr. Y Rama Krishna, MBA, PhD
Members
Dr. Yu Hsing, Southeastern Louisiana University, USA
Prof. Richard J. Cebula, Jacksonville University, USA
Dr. Ilhan Ozturk, Cag University, Turkey
Dr. Mohammad Talha, King Fahd University of Petroleum & Minerals, Saudi Arabia
Dr. Dar-Hsin Chen, National Taipei University, Taiwan
Dr. Krishn A. Goyal, Bhupal Nobles' (PG) College, India
Dr. Michail Pazarskis, Technological Educational Institute of Serres, Greece
Dr. Sorin A Tuluca, Fairleigh Dickinson University, USA
Muhammad Abdus Salam, State Bank of Pakistan, Pakistan
Dr. Huseyin Aktas, Celal Bayar University, Turkey
Dr. Panayiotis Tahinakis, University of Macedonia, Greece
Dr. David Wang, Chung Yuan Christian University, Taiwan
Dr. Nozar Hashemzadeh, Radford University, USA
Dr. Ahmet Faruk Aysan, Bogazici University, Turkey
Dr. Leire San Jose, Universidad del País Vasco, Spain
Prof. Carlos Machado-Santos, UTAD University, Portugal
Dr. Arqam Al-Rabbaie, The Hashemite University, Jordan
Dr. Suleyman Degirmen, Mersin University, Turkey
Prof. Dr. Ali Argun Karacabey, Ankara University,
Dr. Abdul Jalil, Qauid-i-Azam University, Pakistan
Dr. Moawia Alghalith,UWI, St Augustine, West Indies
Dr. Michael P. Hanias, School of Technological Applications, Greece
Dr. Mohd Tahir Ismail, Universiti Sains Malayasia, Malayasia
Dr. Osama D. Sweidan,University of Sharjah, UAE
Dr. Hayette Gatfaoui, Rouen Business School, France
Dr. Emmanuel Fragnière, University of Bath, Switzerland
Dr. Shwu - Jane Shieh, National Cheng-Chi University, Taiwan
Dr. Zhaojun Yang, Hunan University, China
Dr. Abdullah Sallehhuddin, Multimedia University, Malaysia
Dr. Shaio Yan Huang, National Chung Cheng University, Taiwan
Dr. Burak Darici, Balikesir University ,Turkey
Mr. Giuseppe Catenazzo, HEC-University of Geneva, Switzerland
Dr. Hai-Chin YU, Chung Yuan University, Taiwan
Prof. J P Singh, Indian Institute of Technology (R), India
Prof. David McMillan, University of St Andrews, Scotland UK
Dr. Jorge A. Chan-Lau, International Monetary Fund, Washington DC
Prof. José Rigoberto Parada Daza, Universidad de Concepción, Chile
Dr. Ma Carmen Lozano, Universidad Politécnica de Cartagena, Spain
Dr. Federico Fuentes, Universidad Politécnica de Cartagena, Spain
Dr. Mohamed Saidane, University of 7 November at Carthage, Tunisia
International Research Journal of Applied Finance ISSN 2229 – 6891
Vol – II Issue – 6 June, 2011
717
Dr. Frédéric Compin, France
Dr. Raymond Li, The Hong Kong Polytechnic University, Hong Kong
Prof. Yang-Taek Lim, Hanyang University, Korea
Dr. Aristeidis Samitas, University of Aegean, Greece
Dr. Eyup Kahveci, Turkey
Dr. Mostafa Kamal Hassan, University of Sharjah, College of Business, UAE
Dr. Anthony DiPrimio, Holy Family University, USA
Dr. Anson Wong, The Hong Kong Polytechnic University, Hong Kong