Using multiple regression analysis, estimate the Fama-French 3 Factor model for your company and interpret your results: Quantitative Methods for Financial Management Course Work, UOM, UK

Assignment Question 1

Download 30 years’ worth of monthly data for the period January 1993 to December 2022 on a company of your choice from the CRSP database via WRDS, and download the Fama-French 3 Factors from the Kenneth French Data Library or from WRDS. You can access the Kenneth French Data Library directly here: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
You will find the Fama-French 3 Factors in the “U.S. Research Returns” section of the website. Note that the returns data from the Kenneth French Data Library is in percentage form. Returns data downloaded from the CRSP database is in decimal form. You should therefore either divide the data from the Kenneth French data library by 100 or multiply the returns on your company that you download from CRSP by 100.

Using multiple regression analysis, estimate the Fama-French 3 Factor model for your company and interpret your results. In interpreting and discussing your results and the fit of your model, you should conduct any hypothesis tests you feel are relevant, as well as undertaking and interpreting diagnostic tests (for example, testing for heteroscedasticity and serial correlation) on the residuals.

In addition to the textbook and the notes, you should find the following papers useful in motivating the analysis and interpreting your results (you do not need to provide a detailed review of the papers in your answer):

French, K.R. and E.F. Fama, 1993, Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3-56. For the purposes of the
assignment, you do not need to worry about the bond factors (sections 2.1.1,
4.1 and 4.3), the discussion of the findings for bond returns, nor the material in
sections 5 and 6 (what Fama and French refer to as diagnostic testing in section 6 can be thought of more as robustness checks.)

French, K.R. and E.F. Fama, 2004, The Capital Asset Pricing Model: Theory and Evidence. Journal of Economic Perspectives 18, 25-46. The sections on “Early Empirical Tests”, “Recent Tests” and “Explanations: Irrational Pricing or Risk” should prove useful.

Assignment Question 2
Download five years’ worth of daily stock return data covering the period January 2015 to December 2019 on a company of your choice.

Using the time series of returns for the company you have chosen, estimate a
univariate time series model (white noise, autoregressive (AR), moving average (MA) or ARMA) that you think best describes the time series you have chosen and interpret your results. Your analysis should include a discussion of why, based on the autocorrelation and partial autocorrelation functions and any additional suitable statistical tests you choose to run, you have identified the model you have and why this might be the most appropriate among the contender models you considered. Are your results consistent with the weak form of market efficiency? Briefly explain why or why not.

Assignment Question 3
From the Kenneth French Data Library, which you can access directly here:
https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

download five years’ worth of daily return data covering the period January 2015 to December 2019 on the market return and two industry portfolios of your choice from the “30 Industry Portfolios” section of the website. If you wish, you can download daily returns on the market from the CRSP database via WRDS. Describe the purpose of a Vector Autoregression (VAR), and estimate and interpret a VAR model describing the dynamic relationship between returns on the market and returns on the industry portfolios.

You should include a discussion of why you have identified the model you have and perform any additional analysis you think may be useful in interpreting the results from your VAR model. In addition to the textbook and the notes, you should find the following paper useful in motivating the analysis and interpreting your results (you do not need to provide a detailed review of the paper in your answer)

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