FINA 6275 INVESTMENT ANALYSIS AND GLOBAL PORTFOLIO MANAGEMENT SPRING 2022 HOMEWORK 1
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HOMEWORK 1 FOR
INVESTMENTANALYSIS AND GLOBAL PORTFOLIO MANAGEMENT
FINA 6275, SPRING 2022
This homework is the first part of a bigger investments project to follow in the Spring in FIN 6275 Investment Analysis and Global Portfolio Management. This homework is about getting the data. The bigger project will involve creating a portfolio of S&P500 sector ETFs and later comparing its performance to a passive investment in the S&P500 index. An ETF [Exchange Trade Funds] is an investment fund that is traded on a stock exchange (just like a share of stock). For example, the ETF with ticker symbol XLF represents a portfolio ofthe financial firms in the S&P500 index.
Your first homework involves collecting return data for the sector ETFs ofthe S&P500 index:
1
2
3
4
5
6
7
8
9
10
XLB
XLE
XLF
XLI
XLK
XLP
XLU
XLV
XLY
XLRE
Materials
Energy
Financials
Industrials
Technology
Consumer Staples
Utilities
Health Care
Consumer Discretionary
Real Estate
Since XLRE has a very short history (from 2015), only collect and analyze the first 9 sector ETFs.
Learning Goals: This homework is intended to make you comfortable with
Due date: January 23, 2022 11:59 pm US Eastern Time (through Blackboard)
Teamwork
Blackboard.
Submission Format:
professional presentation for work. Use colors.
Instructions (use Bloomberg_tutorial to help you with importing the data):
1. From Bloomberg, obtain the monthly total return index series for the 9 sector ETFs above from December 31, 1998 to August 31, 2021.
2. Use the total return indexes to calculate monthly returns for the ETFs.
3. To convert the date column into a number format, in Excel, use “Format/Cell” then choose for ‘Number’ the ‘custom’ format as ‘yyyymmdd’ . This will turn 12/31/1998 into 19981231.
4. Save the Excel spreadsheet into a CSV file, for easier import into Matlab, say ‘HWdata.csv’.
5. Import the data into Matlab:
a. Import the monthly return series
data=csvread(‘HWdata.csv’,1,0)
b. Create the return matrix R, as a TxN matrix,
R=data(:,2:end);
[T,N]=size(R)
%Thefirst column of data is the column of dates, so we skipped itfor now.
c. Create a vector of dates and a vector of names to keep track of which row stands for which month and which column stands for which ETF.
names=[{'XLB'};{'XLE'};{'XLF'};{'XLI'};{'XLK'};{'XLP'};{'XLU'};{'XLV'};{'XLY'}]
d. At this point, you should have three variables in your workspace:
6. Prepare a report to be submitted describing the data. Specifically:
a. Plot the time-series of each ETF return on a separate graph. You can plot them in the same graph in 9 different subplots: use command subplot(3,3,1) for the first one, subplot (3,3,2) for the second one, …
or use a loop
subplot(3,3,i); plot(R(:,i)); set(gca,'Xlim',[1 length(dates)]); title(names(i)'); end
b. Display the histogram of each ETF return on a separate subplot. Again use a loop and
c. Obtain the 9x1 vectors of mean return (r), standard deviation (s), skewness (skew), and kurtosis (kurt) for each ETF.
d. Do these monthly portfolio returns look normally distributed? Do they exhibit skewness or kurtosis? (Hint: Base your conclusion on a visual inspection of the histograms and the statistics obtained in c). You DO NOT need to do normality tests.)
e. Report the variance-covariance (S) and correlation (C) matrices ofreturns.
f. Which ETF returns are the least/most correlated? Are you surprised (why or why not?)
2022-02-24