这个作业是用R语言实现Fama-French三要素模型来预测资产收益

FTEC5580 Project 1
Due 11:59pm, Mar 6, 2020
Instructions:
• Please submit all your files via Blackboard. In your report file, you must include your
name and student ID.
• If you submit your work late, please directly email it to the TA. Late submission incurs
a penalty as specified in the syllabus. Submissions made two days after the deadline are
not accepted.
• You can only use R.
• The report must be written in English.
• You must work on the project independently.
• The TA responsible for grading this project is Christian MEIER.
• A stock has been assigned to you and please find its ticker symbol in the
grade center of Blackboard. Please follow the guideline file posted in the
folder Projects to download data from Yahoo Finance.
Continue to analyze the stock that is assigned to you (you cannot analyze other stocks). In
this assignment, you will consider the famous Fama-French three-factor model for asset returns.
The model assumes that, for a given asset,
Rt − µt = α + β1(RM,t − µt) + β2SMBt + β3HMLt + t
.
Here, Rt
, RM,t and µt are the net returns of the asset, the market portfolio and the risk-free
asset from t − 1 to t, respectively. The market excess return is the first risk factor, while the
second and the third ones are SMB (small minus big) and HML (high minus low). To be more
specific, SMB is the difference in returns on a portfolio of small stocks and a portfolio of large
stocks. “Small” and “big” refer to the size of the market value of a stock. HML is the difference
in returns on a portfolio of high book-to-market value stocks and a portfolio of low book-tomarket
value stocks. SMBt and HMLt are the differences in returns in the t-th period given
by (t − 1, t].
Data: Use daily data of the stock from Jan 3, 2017 to Dec 31, 2019. You are also given daily
data for RM,t − µt
, SMBt
, HMLt and µt complied by Prof. French. Note that all the returns in
the French dataset are without the percentage sign. So to correctly analyze the data, multiply
the daily net returns calculated from your stock prices by 100. You also need to make sure that
the returns data of your stock have the same sample size as the factors data.
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(1) Find a parametric distribution that fits well to the net return data of your stock. Here, let’s
simply ignore that a net return has to be greater than −1 and you can use distributions
living on the real line such as normal to model net returns. In this part, you must show
sufficient analysis to justify your choice.
(2) Train the Fama-French three factor model. Check the model assumptions and address
any problems that occur. Is alpha = 0 for your stock? Which factors are statistically
significant?
(3) Compare the CAPM model and the Fama-French three factor model in terms of adjusted
R2
, Cp, BIC, and CV test error (consider 10-fold CV and LOOCV). Which model is
better? In this part, we can assume the typical assumptions for linear regression are
satisfied.
Files to be submitted:
(1) A file that shows your solutions to each question. Interpretations of the results should be
provided. All plots and tables should go into this file. Name this file as “Last name-first
name-report”, e.g., Li-Lingfei-report.
(2) The CSV file that contains the stock price data. Name this file as “Last name-first namedata.csv”,
e.g., Li-Lingfei-data.csv.
(3) A printout of your R commands in pdf format. In the RGui, just click ”print” and choose
a pdf printer. Name this file as “Last name-first name-print”, e.g., Li-Lingfei-print.
Please follow the naming convention.
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