代写AD 717 Final Project代写C/C++编程

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 their 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 money every year for risky high-growth investments or appropriate short-selling opportunities.

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 Blackboard. Stocks in that file are companies in the S&P 400 as of early January 2025 with five years’ worth of data.

Then, perform. the following exercises:

1. Write two paragraph per stock in your portfolio explaining clearly (i) why this stock is a good choice for your portfolio given the investor profile and (ii) the company’s background. Support your answers with both description of the firm and their business model and appropriate financial ratios.

2. Copy your stocks’ prices from the shared spreadsheet on Blackboard. Compute monthly returns. (Note that you need 61 prices to compute 60 months’ worth of returns).

3. 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.

4. Run a regression of the stocks’ excess returns against the excess market return to find the CAPM beta for each company’s shares.

5. 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. Justify your alpha based on the firm’s business model and financial ratios.

6. Build an active portfolio with the six stocks according to Chapter 27.1 in our textbook.

7. Run a regression of the stocks’ returns against the excess market return, SMB and HML to find the market beta, SMB beta and HML beta. Categorize each company into

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.

8. Run a regression on the portfolio with the weights you find in part 6 against the excess market return, SMB and HML to find the market beta, SMB beta and HML beta of the entire portfolio.

9. Based on your findings and the investment strategy, identify a benchmark portfolio against which you will compare your portfolio. Adjust the benchmark ETF or mutual fund for its riskiness.

10. Consider the mutual fund report above. Recreate the sections in purple for your portfolio. A bigger version of the image can be found at the end of this document.

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.

§ Note that our regression estimate is simply that: an estimate that comes with uncertainty. An estimated βMKT of 0.95 or 1.05 is likely still consistent with a portfolio that has the same 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:

	Coefficient	Std. Error	p-value
MKT	0.876	0.025	0.000
SMB	-0.133	0.051	0.012
HML	0.363	0.031	0.000

§ 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:

	Coefficient	Std. Error	p-value
MKT	0.715	0.065	0.000
SMB	-0.221	0.132	0.100
HML	-0.075	0.079	0.342

§ 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.