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FIN411 Advanced Investments
Semester 2 2023/2024 Group Coursework 1 (15%)
Due date: Week 9 Friday 26th April 2024
INSTRUCTIONS
. Coursework 1 (CW1) is worth 15% of your final grade.
. Each group will comprise 3 to 4 students. The group will receive a score out of 15%. The group members will decide on individual member scores, such that the average score equals the group score. Put simply, I assign the mean, and group members assign the volatility.
. Each group will nominate a captain, who will email me the name and student number of each group member ASAP. On the due date, each group captain will:
o Hand in a hardcopy of the assignment report.
o Email me a softcopy of the assignment report, as well as an Excel file that contains the data, calculation, graphs etc.
. The assignment report should be written in Times New Roman, 12-point font with 1.5 paragraph spacing. LIMIT: 15 pages, including figures and tables.
Portfolio sorting
The aim of this assignment is to expose students to portfolio sorting, which is an essential tool for Investments. CW1 allows students to address empirical issues in beta estimation, different sorting approaches, as well as sample construction.
The group assignment can be a US study, China study, or even an Australian study. For Chinese stock market data, CSMAR (China Stock Market and Accounting Research) would be your first choice. It is a very comprehensive database that should cater to most Masters dissertation topics, across Corporate Finance, Investments, and Financial Markets. The other database that specializes more on Financial Markets would be the WIND Financial Database.
In Workshop 3, I will do a simple demonstration to access the CSMAR database from the library website.
Section 1: Data, sample and descriptive statistics (7%)
a) Collect daily prices for N=100 stocks, across at least 7 different industries, for T=6 years, as well as a suitable index to proxy the market portfolio M.
b) Justify your choice of type of stocks in your firm sample, your chosen sample period, and your chosen index to proxy the market portfolio M.
c) For each stock, use the full sample to compute the average return and volatility as a proxy for (μi, σi). You can use simple functions in Excel, but you would need to annualize both the mean return and volatility. How do you annualize (μi, σi)? Hint: σi is proportionate to JTrading time, so to annualize daily σi , use √252.
d) Using (μi, σi), perform a simple quintile double-sort 5x5: Assume N=100; Sort N on μi into 5 groups, then within each group, further sort on σi into 5 groups. You should have 4 stocks in each of 25 groups, for N=100.
e) For each of the 25 portfolios, generate an equally-weighted daily return and compute the annualized (μP, σP).
f) Plot the location of your 25 portfolios on the (μ, σ) space.
Section 2: Empirical methodology to form beta portfolios (8%)
g) Run a market model regression to estimate each stock’s βi,t on a 1-year rolling window. For T=6 years, and each stock i = 1,2, … 100, you would have 6 estimated beta βi,t for each stock, or 600 estimated betas in total.
h) At the end of Year 1, quintile-sort N on βi,t using breakpoint-sorting: Compute estimated β interval = [βMax 一 βMin]/5 to derive breakpoints; Sort N using these
breakpoints to form 5 β-sorted portfolios = [βP(L), 2, 3, 4, βP(H)]
E.g. [1.5-0.5]/5=0.2. Beta breakpoints = {0.7,0.9,1.1,1.3}
i) Hold the 5 β portfolios to the end of Year 2. At the end of Year 2,
a. Compute each portfolio’s equally-weighted return and beta.
b. Update the beta for each stock i using Year 2 data.
c. Repeat h) to rebalance the β portfolios, and hold to the end of Year 3.
d. Repeat i) until you reach the end of Year 6.
j) Plot the CAPM’s SML using your chosen M, based on the approach in Cochrane (2011). Use the equally-weighted return and beta of each of the 5 beta portfolios from Years 2-6 (5-year horizon) to plot their location on the same diagram.
k) Discuss the position of the 5 portfolios, relative to one another, and to the SML.
Focus more on the location of βP(L) and βP(H) .