For each question you have to provide: An executive report with your results: this report should provide in a concise manner all the results required for a given question. If you have never written an...

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For each question you have to provide:




  • An executive report with your results: this report should provide in a concise manner all the results required for a given question. If you have never written an executive report, you can take a look at this website. The maximum length of the report is one page without counting figures or tables.




  • A Python notebook with calculations supporting your results: include markup cells with com- ments to guide the reader across the document.





Question:Question 2


[10pts] The following exercises are based on the hedge fund index data provided. The underlying data is the same as that used in the exercises for Question 1 and these exercises complement each other.


Suppose that you were running a multi-strategy hedge fund in 2003 and you are allocating capital across various trading groups. Compute the excess return of each of the hedge fund indices and build your report based on the following analyses:




  1. Define portfolio with equal weights (i.e. 1/9) using the first 9 hedge fund styles (not including the overall index called “DJCS Hedge Fund USD”). Compute the excess return of the corresponding portfolio (as the dot product of portfolio weights and excess returns of hedge funds). Finally, compute the Sharpe ratio over the early sample 1994-2003 (that you would have been aware of in 2003), the late sample 2004-2012 (the period over which your returns would be realized), and the full sample.




  2. Compute another weighted average of these 9 hedge fund styles, where the weights are chosen to maximize the Sharpe ratio over the early sample (e.g., update the code we developed in class so that the objective function is the Sharpe ratio; do not use any constraint). What is the SR of this portfolio over the late sample? How does the answer compare to the equal-weighted portfolio of the previous point? Discuss the issues with portfolio optimization and what you might do about it.



hfdata-1ethw4wa.xlsx hfdata-wor5g5o5-2e3z5cuo.xlsx assignment1-bpv14xr1-ltz5w2kq.pdf
Answered Same DayMar 11, 2021

Answer To: For each question you have to provide: An executive report with your results: this report should...

Abr Writing answered on Mar 13 2021
154 Votes
Assignment 1 (1).ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Importing the necessary libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from pandas import read_excel, DataFrame\n",
"from matplotlib import pyplot as plt\n",
"from numpy import dot, zeros, array, random, sqrt, mean, std"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reading the data file and showing the first five data instances"
]
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"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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"
01234
Date1994-01-31 00:00:001994-02-28 00:00:001994-03-31 00:00:001994-04-29 00:00:001994-05-31 00:00:00
RF0.00250.00210.00270.00270.0032
Mkt-RF0.029-0.0263-0.04850.00680.0062
SMB0.00110.0272-0.009-0.0088-0.0205
HML0.0215-0.01370.01290.01660.0012
UMD0.0009-0.0027-0.01310.0039-0.0222
Ln/Sh Eq Hedge Fund USD0.0117334-0.0250108-0.0391143-0.01571960.0055504
Eq Mkt Ntr Hedge Fund USD-0.005466560.00210617-0.002514280.00239738-0.0012286
Ded Sh Bs Hedge Fund USD-0.01626660.01971320.07182670.01271040.0223574
Global Mac Hedge Fund USD0.00143344-0.0568108-0.0428543-0.01603260.0378394
Mngd Fut Hedge Fund USD0.001933440.01166680.02592310.008437140.00749708
Emg Mkts Hedge Fund USD0.105133-0.0117054-0.0462273-0.0836728-0.00745314
Evnt Drvn Hedge Fund USD0.0365334-0.00184983-0.0130513-0.00667262-0.0016036
Cnvrt Arb Hedge Fund USD0.003333440.00118817-0.00975828-0.0253316-0.0103286
Fx Inc Arb Hedge Fund USD0.0127334-0.0203458-0.0169303-0.002164620.0077804
DJCS Hedge Fund USD0.0111334-0.0412398-0.0357773-0.01754160.0221784
\n",
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"
],
"text/plain": [
" 0 1 \\\n",
"Date 1994-01-31 00:00:00 1994-02-28 00:00:00 \n",
"RF 0.0025 0.0021 \n",
"Mkt-RF 0.029 -0.0263 \n",
"SMB 0.0011 0.0272 \n",
"HML 0.0215 -0.0137 \n",
"UMD 0.0009 -0.0027 \n",
"Ln/Sh Eq Hedge Fund USD 0.0117334 -0.0250108 \n",
"Eq Mkt Ntr Hedge Fund USD -0.00546656 0.00210617 \n",
"Ded Sh Bs Hedge Fund USD -0.0162666 0.0197132 \n",
"Global Mac Hedge Fund USD 0.00143344 -0.0568108 \n",
"Mngd Fut Hedge Fund USD 0.00193344 0.0116668 \n",
"Emg Mkts Hedge Fund USD 0.105133 -0.0117054 \n",
"Evnt Drvn Hedge Fund USD 0.0365334 -0.00184983 \n",
" Cnvrt Arb Hedge Fund USD 0.00333344 0.00118817 \n",
"Fx Inc Arb Hedge Fund USD 0.0127334 -0.0203458 \n",
"DJCS Hedge Fund USD 0.0111334 -0.0412398 \n",
"\n",
" 2 3 \\\n",
"Date 1994-03-31 00:00:00 1994-04-29 00:00:00 \n",
"RF 0.0027 0.0027 \n",
"Mkt-RF -0.0485 0.0068 \n",
"SMB -0.009 -0.0088 \n",
"HML 0.0129 0.0166 \n",
"UMD -0.0131 0.0039 \n",
"Ln/Sh Eq Hedge Fund USD -0.0391143 -0.0157196 \n",
"Eq Mkt Ntr Hedge Fund USD -0.00251428 0.00239738 \n",
"Ded Sh Bs Hedge Fund USD 0.0718267 0.0127104 \n",
"Global Mac Hedge Fund USD -0.0428543 -0.0160326 \n",
"Mngd Fut Hedge Fund USD 0.0259231 0.00843714 \n",
"Emg Mkts Hedge Fund USD -0.0462273 -0.0836728 \n",
"Evnt Drvn Hedge Fund USD -0.0130513 -0.00667262 \n",
" Cnvrt Arb Hedge Fund USD -0.00975828 -0.0253316 \n",
"Fx Inc Arb Hedge Fund USD -0.0169303 -0.00216462 \n",
"DJCS Hedge Fund USD -0.0357773 -0.0175416 \n",
"\n",
" 4 \n",
"Date 1994-05-31 00:00:00 \n",
"RF 0.0032 \n",
"Mkt-RF 0.0062 \n",
"SMB -0.0205 \n",
"HML 0.0012 \n",
"UMD -0.0222 \n",
"Ln/Sh Eq Hedge Fund USD 0.0055504 \n",
"Eq Mkt Ntr Hedge Fund USD -0.0012286 \n",
"Ded Sh Bs Hedge Fund USD 0.0223574 \n",
"Global Mac Hedge Fund USD 0.0378394 \n",
"Mngd Fut Hedge Fund USD 0.00749708 \n",
"Emg Mkts Hedge Fund USD -0.00745314 \n",
"Evnt Drvn Hedge Fund USD -0.0016036 \n",
" Cnvrt Arb Hedge Fund USD -0.0103286 \n",
"Fx Inc Arb Hedge Fund USD 0.0077804 \n",
"DJCS Hedge Fund USD 0.0221784 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hf_data = read_excel('hf_data.xlsx', skiprows=15)\n",
"hf_data = hf_data.drop(columns = ['Unnamed: 1', 'Unnamed: 3', 'Unnamed: 8'])\n",
"hf_data = hf_data.rename(columns = {'Unnamed: 0':'Date'})\n",
"hf_data.head().T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part (a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assuming equal weights to first 9 hedge funds taking the average of the risk across all the selected hedge funds and then printing the descriptive statistics"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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"
returns
count222.000000
mean0.005438
std0.013324
min-0.062592
25%-0.000827
50%0.006629
75%0.013113
max0.043456
\n",
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"
],
"text/plain": [
" returns\n",
"count 222.000000\n",
"mean 0.005438\n",
"std 0.013324\n",
"min -0.062592\n",
"25% -0.000827\n",
"50% 0.006629\n",
"75% 0.013113\n",
"max 0.043456"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = DataFrame(hf_data.iloc[:,6:15].mean(axis=1))\n",
"data.columns = ['returns']\n",
"data.index = hf_data.Date\n",
"data.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plotting the time series"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png":...
SOLUTION.PDF
SOLUTION.PDF

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