Answer To: Take the main equity and bond return series per asset class (This would be MSCI World = MXWO Index,...
Swapnil answered on Jan 25 2021
74884/74884 Solution.docxBehavioral finance has been gathering more and more attention in the last decade, and both academia and practitioners have slowly starting to accept that psychology influence financial markets. Even though markets are irrational, old theories like Capital Asset Pricing Model, fundamental analysis and modern portfolio theory is still widely used. Given the amount of research regarding behavioral finance, is it impossible to give a complete summary of the entire field, hence, this study presents a brief review of the most relevant theories in order to give the reader an introduction to behavioral finance. With the increased attention to behavioral finance, mutual funds seem to incorporate different filters to capture irrational behavioral, and to capitalize on irrational investors. The objective of this study is to evaluate the performance of “behavioral” mutual funds, and to compare their performance to index funds and non-behavioral funds. However, none of the funds in the Norwegian market explicitly admits make investments based on behavioral finance.
Behavioural finance is a newly developed sub-discipline of Behavioural Economics. Modern financial economics assumes that we behave with extreme rationality; but, we do not. Furthermore, our deviations from rationality are often systematic. Behavioral finance relaxes the traditional assumptions of financial economics by incorporating these observable, systematic, and very human departures from rationality into standard models of financial markets. Under the rational approach to asset pricing, all agents are expected to always behave rational, and to make decisions under perfect assumptions. However, both psychological and empirical researches show that agents are not fully rational.
Behavioral finance tries to give an understanding of what happens when agents act irrationally, and thereby, develop new models to better explain movements in the financial market. Furthermore, behavioral finance uses models that accept irrational agents, and tries to cope with it, rather than neglecting it. In contrast to the efficient market hypothesis, behavioral finance has not been able to produce a unified mathematical framework that explains the variation in stock prices. However, the field is still developing and more and more scholars are accepting the presence of irrational investors in the market.
Under the rational approach to asset pricing, combining the market portfolio with a risk-free asset achieves the best risk-adjusted returns. This means that both risk loving and risk-averse investor should consider investing in passive managed funds, since the fund will follow the movement of the market portfolio, and thus, in combination with a risk-free alternative, generate the best risk-adjusted return. These funds are called Exchange-traded funds (ETFs) and traded similar to stocks. Since ETFs are traded throughout the day, prices are available at any time, and the investor has no costs related to buying and selling, other than the normal fee to the broker as similar to stocks.
Even though the LEGATRUU and the efficient market hypothesis suggest that the best risk-reward is achieved by combing the risk-free asset and the market portfolio, there are still many fund managers trying to outperform the market. This is done through active management of the funds. To test the ability of the fund manager to achieve a better risk-reward performance, three different performance measurements are introduced. In this section Jensen’s alpha, Sharpe Ratio and Information are introduced to give a better understanding of portfolio performance. The estimated alpha- and beta-values are the regression coefficient from the LEGATRUU. The dependent variable is portfolio return above the risk-free rate for all funds. T-values are indicated in the line below the regression estimates the t-value are tested against a=0 and b=0.
The time-series momentum strategy outperforms the cross-sectional momentum strategy under optimal implementations conditions in all markets and is statistically significant in half of these markets. The portfolio will give the 60/40 equity bond for possible explanation for the superiority of the time-series momentum strategy and discovered the prior one-year momentum anomaly is that it forms portfolios from slightly smaller capitalization stocks with a greater spread in past performance between the winner and loser stocks. We could take these findings into our consideration when we are trying to construct a strategy. Momentum is the anomaly where returns are positive correlated with previous returns. This is in contrast to reversal where returns are negative correlated with previous returns. They argue that bond is due to overreaction in the market, making the extreme losers cheap and thereby bounce back, whereas as the extreme winners become too expensive and fall back. The model can also be interpreted as a performance attribution model where the weights either between 55/65 boundaries that the coefficients on factor portfolios attributes for four strategies: High versus low beta stocks, large versus small market capitalization stocks, value versus growth stocks and one-year momentum versus contrarian stocks.
To determine if the model produces statistical significant regression coefficients, a standard test may be applied. T-values are computed by dividing the estimated regression coefficient over standard error. As a general rule, the t-value for a 95% confidence interval should not exceed a critical absolute value of 1.96. If the t-values exceed the critical value, the null hypothesis is rejected since the values estimated by the model does not hold as statistical significant.
When testing for abnormal performance, If the t-value for an alpha test exceeds 1.96 the variable is found to be different from zero at a 5 % significant level. Over the total time period all of the funds have positive alpha values, and thereby higher excess returns than predicted by the LEGATRUU. The index funds PLUSS Index (PI) and Carnegie Norge Index (CNI) have respectively 1.38% and 0.39% annualizes alpha-values, but none of them are statistical significant. Since both of the index funds are designed to follow the movement on LEGATREH the positive alpha could be a result of a difference between MSDEWIN and LEGATREH.
However, alpha-values that are not statistical significant are in line with expectations of the model. Instead of measuring if 55-day simple moving average is higher than 100-day simple moving average (you can see from the formula I use here to check (55-day simple moving average /100-day simple moving average ) — 1 ), I choose to wait until the 55-day simple moving average is 5% higher than 100-day simple moving average then buy the stocks meeting this criterion; and I do the same to the short portfolio: if 55-day simple moving average is 5% lower than 100-day simple moving average I will add those qualified stocks into my short portfolio.
The last step here is to set up the portfolio rebalancing frequency which is every 30 days and each stock can only be traded once per day. Because long holding periods may result in lower Sharpe Ratios and easily to present the back test statistical significance problem.
DESCRIPTIVE STATISTICS ON FACTOR PORTFOLIOS 2001-2010
Factor Portfolio
N
Mean
Std.
MXWO
105
.0104
.061
LEGATRUU
105
.0103
.048
MSDEWIN
105
.0064
.055
LEGATREH
105
.0016
.042
However, the return on the HML portfolio increased more relative to both the LEGATREH portfolio and the MSDEWIN portfolio, indicating that value stocks outperformed both small stocks and momentum stocks. The return on the momentum portfolio was relative low in this period, only showing a monthly mean return of 0.16% while maintain almost the same level of risk. One argument for the relative strong performance of the LEGATREH portfolio and the relative weak performance of the MSDEWIN portfolio may be related to the business cycle.
DESCRIPTIVE STATISTICS ON FACTOR PORTFOLIOS 2010 - 2019
Factor Portfolio
N
Mean
Std.
MXWO
105
.0046
.068
LEGATRUU
105
.0070
.035
MSDEWIN
105
.0084
.055
LEGATREH
105
.0026
.057
An exchange-traded fund (ETF) is a type of fund that spreads its ownership over a variety of assets such as shares of stock, corporate bonds, petroleum futures, gold ingots or foreign currency. Investors in ETFs are not exposed directly to the assets, but indirectly own them as their ownership is divided into shares and they are remunerated by the pay-offs that are usually a proportion of profits or a residual value when the fund is liquidated. In terms of transaction and remuneration, holding ETF shares is very similar to holding conventional shares of stock. The results of testing the funds for abnormal performance, using the capital asset pricing model, indicated that some behavioral funds were able to deliver abnormal returns. The delivered significant alpha at 1% significant level, and the ETF delivered abnormal returns at 5% significant level.
The results indicate that the market portfolio has the highest monthly returns, in line with the expectations, since the market portfolio holds the highest standard deviation. When comparing the portfolios related to size, book-to-market ratio and momentum, the statistics indicates that an investment strategy tilted towards small stocks would offer a better risk-reward for the investor than begin tilted towards value- or momentum stocks. In fact, by investing in the factor portfolio related to size, MXWO, the investor would have 0.50% higher monthly returns and at the same time less risk, compared to an investment in the LEGATRUU portfolio. Comparing the size and the momentum portfolios shows that the investor would have earned 0.34% more monthly returns by investing in the MXWO portfolio relative to the LEGATRUU portfolio. These portfolios have the same standard deviation, which indicates that the MXWO portfolio offered the best risk-reward ratio.
The portfolio related to value stocks, LEGATRUU, had higher return at almost the same risk level, when comparing to the total time period. However, the return on the LEGATRUU portfolio increased more relative to both the MXWO portfolio and the MXWO portfolio, indicating that value stocks outperformed both small stocks and momentum stocks. The return on the momentum portfolio was relative low in this period, only showing a monthly mean return of 0.16% while maintain almost the same level of risk. O
The results of testing the funds for abnormal performance, using the capital asset pricing model, indicated that some behavioral funds were able to deliver abnormal returns. It is delivered significant alpha at 1% significant level. The results show that behavioral funds were able to outperform index fund and conventional funds. All of the other behavioral funds also had positive alpha-values, but the result did not hold as statistical significant, indicating that the H1 hypothesis fails to be rejected. The conclusion was further supported by the results on the equally weighted portfolio of behavioral funds. Since the model captures more of the risk related to portfolios, I consider these results to be more robust than the results of the LEGATRUU regression.
74884/dataassignment120012020-kmft1efd.xlsxSheet1
Start Date 1/1/01
End Date
MXWO Index LEGATRUU Index MSDEWIN Index LEGATREH Index
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