Finance代写

我们总结了Boston University Finance代写里，Term Project AD 717的经典案例：

Final Project

For your term project, you are going to build a portfolio of 5 stocks and write a prospectus of your minifund.

Consider the following three investors:

•

Bryant is a 25-year old young professional, employed in a major city in the northeast. Since

joining the workforce three years ago, he contributes 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”, he has 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 5 stocks. Your stocks

must be listed on a US stock exchange and their IPO must be more than five years ago. Then, perform

the following exercises:

- Write two paragraphs per stock in your portfolio explaining clearly why this stock is a good choice

for your portfolio given the investor profile. Support your answers with both description of the firm

and their business model and appropriate financial ratios. - Download five years of monthly stock prices from October 2018 to October 2023 and compute the

monthly returns from November 2018 to October 2023. (You need to download 61 prices to

compute 60 returns). - Download the file https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/FF_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. - 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 models and financial ratios. - Build an active portfolio with the 5 stocks

according to Chapter 27.1 in our textbook. - 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. - 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. - Based on your findings and the investment

strategy, identify a benchmark portfolio against which you will compare your portfolio. - Recreate the sections in purple for your portfolio. A better copy of the mutual fund sheet can be

found in Chapter 4.8 of our textbook.

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

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.

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:

MKT

SMB

HML

▪

▪

▪

o

Coefficient

0.876

-0.133

0.363

Std. Error

0.025

0.051

0.031

p-value

0.000

0.012

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.

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

MKT

SMB

HML

▪

▪

▪

Coefficient

0.715

-0.221

-0.075

Std. Error

0.065

0.132

0.079

p-value

0.000

0.100

0.342The 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.

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