Term Project AD 717

Final Project

For your term project, you are going to build a portfolio of six stocks and write a prospectus of your mini fund.

Consider the following three investors:

•    Kim is a 25-year-old young professional, employed in a major city in the northeast. Since joining the workforce three years ago, they contribute as much money as possible to his retirement accounts which is invested in a diverse set of index funds. An avid fan of Benjamin Graham's "The Intelligent Investor", they have decided to consider a few individual stocks of companies with good and stable long-term prospects as well as a great management.

•    Nicole is 52 years old, and a few months ago, she retired from her well-paying job after aggressively saving and investing her money prudently for much of her life. While she could go back to work if necessary, she prefers her financial independence. In order to maintain a steady cash-flow, her portfolio is heavily geared towards high yielding stocks, allowing her and her family to live of dividend payments for the most part. Aware of the downturn of General Electric and their dividend cut, she focuses on companies from which she expects a solid and steady dividend growth.

•    Peter is in his mid 30s. He did not start a well-paying job until two years ago, and therefore, he is behind on his retirement savings. To make up for lost time, he is contributing the maximum allowed to his individual retirement account (IRA), which is invested in market ETFs. Additionally, he sets aside $10,000 every year for risky high-growth investments.

Select one of these investors as your client for whom you create the portfolio of six stocks. Your stocks must be in the stock price file on the blackboard under term project (S&P 400 Blackboard.xlsx). Stocks in that file are companies in the S&P 400 as of early October 2024 with five years’ worth of data.

Then, perform. the following exercises:

1.    Explain your criteria to filter the S&P 400 to fit your investor’s goals and objectives. Make sure you justify why these criteria are appropriate for your investor. Then explain the criteria you use to identify the best stocks in this subset. Your criteria should align with the investor’s preferences, but you may get creative.

2.    Write two paragraphs per stock supporting your selections with the company’s background and business model. Support your answers with qualitative and quantitative data, including, but not limited to, a discussion of management, corporate performance and profitability metrics.

3.    Copy your stocks’ prices from the shared spreadsheet on blackboard into your own spreadsheet

Compute monthly returns. (Note that you need 61 prices to compute 60 months’ worth of returns).

4.    Download the file https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-

F_Research_Data_Factors_CSV.zip. In this file, you find excess market return, SMB, HML and the risk-free rate. Use the risk-free rate to compute the excess returns for your stocks. This file has also been copied to blackboard under term project (FamaFrenchData_2024.xlsx).

5.    Run a regression of the stocks’ excess returns against the excess market return to find the CAPM beta for each company’s shares. You can check your result against Bloomberg or Yahoo Finance.

6.    Make a forecast for the alpha of each stock, that is, the return that you expect the stock to perform minus the  return predicted by the CAPM. Your forecast should be justified based on your analysis in Part 1.

7.    Build an active portfolio with six stocks according to Chapter 27 in our textbook.

8.    Run a regression of the portfolio returns (using the weights you find in the previous step) against the excess market return, SMB and HML to find the market beta, SMB beta and HML beta of the entire portfolio. Categorize your portfolio according to

a.    defensive, neutral or aggressive for the market beta;

b.    small, neutral or big for the SMB beta;

c.    value, neutral or growth for the HML beta.

9.    Based on your findings and the investment strategy, identify a benchmark portfolio against which you will compare your portfolio. Adjust the benchmark portfolio’s risk as discussed in Lecture 11.

10. Consider the mutual fund report above. Recreate the sections in purple for your portfolio. A bigger version of the image may be found at the end of this document. Enhance your analysis with the portfolio analytics on the Bloomberg terminal, using the function PRTU.

Term Project Feedback: As you work on your term project, I am available for feedback if you run into problems. The feedback is intended to point out ‘red flag’ or obvious errors, it is not intended to be an ongoing back and forth revising your project. Feel free to reach out to me. I’m happy provide feedback up to Thursday May 1, 2025.

Notes on Fama-French Factors:

•    The Fama-French regressions give you a coefficient for the market risk of a stock or portfolio (βMKT ), its exposure to the risk proxied by the size factor (βSMB ) and its exposure to the risk proxied by the    value factor (βHML ).

•    The interpretation of βMKT  is the same as before:

o If an asset’s estimate for βMKT  is 1, then it has the same market risk as the market portfolio.

o If an asset’s estimate for βMKT  is less (greater) than 1, then it is a defensive (aggressive) investment with respect to market risk.

•    The interpretation of βSMB  is as follows:

o If an asset’s estimate for βSMB  is greater than 0, i.e., positive, then it behaves more like a portfolio that is long small companies and short big companies.

o If an asset’s estimate for βSMB  is less than 0, i.e., negative, then it behaves more like a portfolio that is short small companies and long big companies.

o If an asset’s estimate for βSMB  is indistinguishable from 0 because it’s p-value is greater than

0.05, then the assets is balanced with respect to firm size as measured by market cap.

•    The interpretation of βHML  is as follows:

o If an asset’s estimate for βHML  is greater than 0, i.e., positive, then it behaves more like a portfolio that is long value firms and short growth firms.

o If an asset’s estimate for βHML  is less than 0, i.e., negative, then it behaves more like a portfolio that is short value firms and long growth firms.

o If an asset’s estimate for βHML  is indistinguishable from 0 because it’s p-value is greater than

0.05, then the assets is balanced with respect to value vs. growth.

•    Examples using 5 years of monthly data from 2018 to 2022:

o VTV ETF, capturing large value firms in the US market:

The estimate for βMKT  is 0.876, which is slightly below 1. We may classify this ETF as neutral to moderately defensive

The estimate for βSMB  is -0.133, which is negative with a p-value of 0.012, i.e., less than

0.05. We classify this ETF as behaving more like a portfolio short small firms and long big firms. We may also say the portfolio tilts slightly towards big firms since the coefficient  is small in magnitude.

The estimate for βHML  is 0.363, which is positive with a p-value of practically 0.000, i.e.,   less than 0.05. We classify this ETF as behaving more like a portfolio long value firms and short growth firms. We may also say the portfolio tilts towards value stocks since the coefficient is moderately big in magnitude.

o XLV ETF, capturing the Health Care sector in the S&P 500:

The estimate for βMKT  is 0.715, which is below 1. We may classify this ETF as moderately defensive

The estimate for βSMB  is -0.221, which is negative with a p-value of 0.100, i.e., not less than 0.05. We classify this ETF as behaving like a portfolio that is neither overweight in small or big firms - or as neutral in the size factor.

The estimate for βHML  is -0.075, which is negative with a p-value of practically 0.079, i.e., not less than 0.05. We classify this ETF as behaving more like a portfolio that is  neither overweigh in value firms nor growth firms - or as neutral in the value factor.