FNCE-317 – Introduction to Corporate Finance - Risk, Return, and CAPM - Finance Assignment Help

Assignment Task

This assignment is provided with time for you to work on it during the break. Part 2 will be related to material from the next sections and will be released later. I will first provide a summary of the task and then more detailed instructions to guide you through. Finally, I will describe what to hand in. Please work in groups of 3 or 4. I can help you make a group.

Summary

Choose four real securities and gather five years of monthly trading history for them. In data science it is common to train a model on a first set of data, then test it on the second set. Using only the first 3 years of data, chart the risk-return profile of each security as well as a variety of fixed-weight portfolios constructed from the constituents. Assume a risk-free return of 1% per year. Find the tangent portfolio (what are the weights?) and include the capital market line on the risk-return chart. Compute the beta for each security with respect to the Tangent Portfolio in two ways: as the slope of the regression between security returns and Tangent portfolio returns and by using the covariance-based formula. For one of your securities draw a horizontal line on the chart connecting the security risk-return to the CML to illustrate the interpretation of beta as a “weight” in a mini-portfolio made up of the risk-free asset and the Tangent Portfolio. Chart the Security Market Line that relates beta to return for all four securities and the Tangent portfolio. This chart shows that with total foresight of the risks and returns the CAPM formula holds with the careful choice of Tangent Portfolio. What is the meaning of the slope of the SML in this chart?

Detailed Instructions

1. Choose four real securities and gather five years of monthly trading history for them.

a. Use Yahoo finance, historical data

b. Pick securities with reasonable small positive returns, not too hot. Pick things that are somewhat diversifying, i.e. different from each other. Stocks are good ideas, but also perhaps a bond ETF or Gold or BTC for those enthusiasts (For BTC, I divided all returns by 4 to make it work).

c. Select monthly, 5Y

d. Download to Excel

e. Yahoo adjusts for dividends (and splits) so you can ignore dividends

f. Compute monthly returns on the “adj close” column: P1/P0 - 1

g. Paste this column for all your securities into 1 excel table

2. In data science it is common to train a model on a first set of data, then test it on the second set. Using only the first 3 years of data, chart the risk-return profile of each security as well as a variety of fixedweight portfolios constructed from the constituents.

a. Use the formulas STDEV and AVERAGE

b. Since the data is monthly, you can annualize by multiplying by 12 for the returns and multiplying by sqrt(12) for the risk. (Trust me on this for now: basically, the variance grows by a factor of 12, but Stdev is square root of variance). Put these above the data where you can see it.

c. Remember to only use the first three years of data

d. We will make a new column for blends of these monthly returns. Each month is a product of the returns and weights. Use SUMPRODUCT or MMULT. These are the same thing except for how the data is lined up, I found MMULT useful. The idea is that we rebalance each month to the fixed weights, then get the resulting blended return each month.

What should I hand in? Do not hand in your Excel. We are looking for concise exposition (in a PDF like this one) of what you did including paragraphs and some charts. What worked? What was surprising? What choices did you have to make? Label your charts. You can do this in Excel, or perhaps paste into PowerPoint and you have more freedom to edit there. You are welcome to include new charts that I did not describe such as the classic total return chart over the five years. A variant of this is a total return chart of the CAPM mini portfolio returns versus the actual security returns (see below). The match is pretty good during the calibration period (we designed it that way), and less good in the “future”. In this example, the blue line is 84% TP and 16% Risk-free, i.e. the beta is 0.84.

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