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FIN41360: Portfolio and Risk Management

Individual Assignment 1
(Please read all instructions and notes very carefully)

Portfolio Choice and Performance Attribution

March 18, 2021

Required tasks:
1. Familiarize yourself with the content of the data library on Professor Kenneth
French’s webpage by reading the online legends and help. The web address of the
data library is the following:
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

2. Then, select data from January 1963 to December 2020 on the 10 industry portfolios
(value-weighted) and carry out the following analysis:
a. Sketch the monthly MV efficient frontier of these portfolios using first the
sample estimates of the required means and variance-covariance matrix and
subsequently the Bayes-Stein counterpart by shrinking only the mean.
b. On the frontiers you constructed, highlight the points corresponding to the
global minimum variance portfolio and to the tangency portfolio, assuming
a risk-free rate equal to its average over the sample period. For these
portfolios, also compute and report the mean excess-return, volatility, and
Sharpe ratio.
c. Compare and contrast the two frontiers you constructed (the frontier based
on sample estimates and their Bayes-Stein counterparts). How would you
explain their differences?

3. Select data for the appropriate sample period on the risk-free rate (included among
the so-called “Fama and French factors”, and available for download from the same
data library) and sketch the monthly MV efficient frontier for the 10 industry
portfolios and the risk-free asset, using sample estimates of the required means and
variance-covariance matrix. Compare and contrast this frontier to the ones
previously constructed, after having explained the sample period for which you
obtained data on the risk-free rate.

4. Repeat the analysis required in the item just above (i.e., in question 3) using the
Fama and French factor-mimicking portfolios, also available from the webpage of
Prof Kenneth French, in place of the industry portfolios. Do so first using the so
called “Fama/French 5 Factors (2x3)” mimicking portfolios (See Fama and French,
2014, “A Five-Factor Asset Pricing Model” for a complete description of the factor
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returns). Compare the resulting efficient frontiers to the ones previously obtained.
How would you explain their differences?

5. Re-estimate the monthly frontiers for the 10 industry portfolios and for the
“Fama/French 5 Factors (2x3)” mimicking portfolios over two contiguous time
periods of equal length, one ending in December 1991 and the other one ending in
December 2020, including the risk-free asset in the investment opportunity set. Pick
at least one portfolio from each of the frontiers estimated in the first period and, for
the portfolios thus selected, compare the performance during this period to the
performance during the second period. Do these portfolios remain on the efficient
frontier out-of-sample? Conduct the comparison using the ‘all-time classic’ Jobson
and Korkie (1981) test for the equality of the Sharpe ratios (the test statistic is
defined in footnote 20, page 271 of the Jorion (1985) article) as well as the more
modern test that does not require either the i.i.d. assumption or normality (check out
the article by Ledoit and Wolf (2008) and the accompanying Matlab code available
in the Brightspace page of the course).

6. Then, for at least some of the assets previously considered, repeat the above analysis
subject to any constraint and/or by applying any method, approach or technique
(e.g., adding assets to better exploit the benefits of diversification, relying on the
global minimum variance portfolio, resampling, etc.) that you believe might be of
interest and might offer valuable insight from an investment management point of
view.

Provide a soft copy report containing your findings, which should be appropriately tabulated so
as to maximize their legibility, any required evaluation and discussion of your findings and,
when appropriate, a description of the methodology adopted. The report should be submitted
via the submission folder on Brightspace by 9 April 2021 (midnight).

The maximum length of the report should be 3000 words, including tables and figures (and
everything else). Do not neglect to cite references as appropriate (of course, the bibliography is
included in the word count). To stay within the word count, it will be important to organize
your report appropriately (e.g., making appropriate use of tables, avoiding repetitions, etc.).

Workings by way of MS Excel spreadsheets or Matlab code and data should be e-mailed to my
e-mail address: emmanuel.eyiah-donkor@ucd.ie. Although grades are solely based on your
report, the workings are required to verify all calculations. In the email title, you should indicate
FIN41360: Assignment 1, followed by the student number in brackets.






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Notes:

• The required tasks in the above list are, to a large extent, in a sequential order, in the
sense that the initial ones are necessary for the subsequent ones. Hence, if you run out
of time, it is better to carry out the earlier ones to the best of your ability rather than
trying to do everything to a lesser standard.
• Clarity is of paramount importance and lack thereof will be penalized heavily.
Essentially, as in real world endeavours, unclear answers and discussion will amount to
not having provided the required answer/discussion. Hence, try and privilege clarity and
quality over quantity. For example, if you are running out if time, it is better to address
well a few questions than to attempt to address them all in a poor and, hence, necessarily
obscure manner.
• All the relevant information for the assignment is provided in this document and the
course outline. If an aspect of the analysis is not specified in either document, it means
that it is left for students to make a choice on it, in light of the theory covered in the
course.
• Students are required to make and motivate any such choice relying on the insight
offered by the theory covered in the course and relevant references. The soundness of
such choices, evaluated in the light of available theory and empirical evidence, will be
assessed, and contribute to the overall mark. The more advanced aspects of the
assignment also require a certain amount of autonomous research, which will play an
especially important role in the assignment of higher marks.
• Submissions should contain, as appropriate, a literature review. The latter should strive
to offer a comprehensive and systematic, yet succinct, review and discussion of
academic and practitioners’ contributions to the body of knowledge on issues and topics
examined during the course that are relevant to address the issues at hand, with the aim
to expand and offer additional substantial insight compared to the insight and level of
knowledge developed by the course material (including lectures).
• In preparing the report, try and replicate as closely as possible the layout and structure
(though subject to the applicable maximum length restriction) of academic papers that
evaluate the performance of alternative portfolio construction methodologies. A good
yet nice and readable example is the article by Stevenson (2002) titled “Ex-Ante and
Ex-Post Performance of Optimal REIT Portfolios” (among the readings in Brightspace).
You might also take heed from industry reports that do something similar, which often
have nice and witty ways of making insightful points, though strive to aim at a level of
clarity and rigor comparable to the academic papers. The paper by Rob Arnott of
Research Affiliates (titled “How Can Smart Beta Go Horribly Wrong”) is a good
example of clear and compelling industry report:
https://www.researchaffiliates.com/en_us/publications/articles/442_how_can_smart_b
eta_go_horribly_wrong.html
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Please keep in mind however that, if you lack the insight of an industry maverick (that
comes with many years of first-hand experience), the academic format might be a safer
option as it is designed to facilitate making a point in an effective way relying only on
research, without needing any special experience of the subject matter. An academic
researcher gains insight from research, not necessarily experience, so it is a situation
closer to the one of a typical student.
• Appropriate use of tables and figures, as in academic papers and rigorous industry
reports, is of crucial importance to attain the required clarity. They should be
thoughtfully designed to maximize clarity and impact. For example, annotate your
tables and figures to help the reader gain an immediate understanding of the findings
reported therein. Also, try and condense your findings in as few tables and figures as
possible, to help the reader see the overall picture emerging from your study.



















































































































































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