不限代写-FI506E
时间:2021-11-20
Rennes School of Business
FI506E Quantitative Finance
Instructors:
Emmanuel Eyiah-Donkor
Olga Pakulyak
Empirical Group Project
(Due Date: Friday, 26 November 2021 at 23:59 CET)
Project Details
The objective of this project to is determine whether there is the possibility to ex-
ploit the following assigned anomalies in international equity markets.
Groups 1 and 2 have to examine the value anomaly, measured as the ratio of book
value of equity to market value of equity or book-to-market ratio (BE/ME ) of the
asset (see Rosenberg, Reid & Lanstein, 1985; Fama & French, 1992; Asness, Moskow-
itz & Pedersen, 2013). For example, does a portfolio of high BE/ME ratio firms
perform better or worse than a portfolio of low BE/ME ratio firms?
Groups 3 and 4 have to examine the momentum anomaly, measured as the past
12-month cumulative return of the asset (Jegadeesh & Titman, 1993; Asness et al.,
2013). For example, does a portfolio of high momentum firms perform better or
worse than a portfolio of low momentum firms?
This project is part of your continuous assessment for FI506E Quantitative Finance.
First you should do a literature search and review some relevant studies. For ex-
ample, some literature on anomalies in general is detailed in the References below.
Second, you should obtain data from Bloomberg (the terminals are located in the
Trading Room in Building 1), use some procedures and software so as to determine
if there is an anomaly, and illustrate how this could be exploited.
You should explain all assumptions and actions that you take. It is really important
that you link to the materials that relate to the project in the Quantitative Finance
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module. For instance, materials covered under the asset pricing frameworks, market
efficiency, and various anomalies should be looked at. Discussion of theory, methods,
data, and results should be examined in terms of previous literature and possible
trading strategies. You might look to identify the effect by incorporating features
that have not been completed to exhaustion and incorporate (e.g., theoretical issues)
that may be unique to your project.
As a guide, you could, for example, follow the following steps:
1. Obtain some relevant data (e.g., equity prices, shares outstanding, book value
of equity/shareholders equity, etc) for select companies/firms for a time period
(e.g., January 1999 to December 2020, etc) from Bloomberg. Please note there
is a limit to the numbers of companies (and related information) you can
download for the group project. Bloomberg places this limit on each terminal,
and exceeding the download limit will make the terminal inactive. If this
happens the project cannot be completed. To overcome this issue, we have
posted a file “Company List.xlsx” on Moodle. This file contains a large cross
section of very liquid MSCI ACWI index (covers 23 developed and 24 emerging
markets) stocks. You are only to use companies from this file. A submission
with other companies not in the file will not be accepted. Note that you do
not have to use all the companies in the file and you can pick and choose as
you wish (for example, 500 - 1000 companies). All data must be denominated
in U.S. dollars.
2. As proxy for the risk-free asset, use the rate of return on the U.S. 1-month
Treasury bill (see the Fama/French Developed 3 Factors). The data can be
download from Professor Kenneth French’s Data library at the website at
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data lib
rary.html.
3. Calculate the measure/variable of concern, that is book-to-market equity ratio
or momentum, for each company in your sample. You should read the relevant
articles for guidance on how to calculate it, which will also inform what data
to collect in 1 above.
4. Rank/Sort stocks according to your measure and form quintile portfolios. You
should generate a time-series of monthly equally-weighted and value-weighted
portfolio excess returns (see the references list for guidance on how to rank
stocks and form portfolios. Knowing how to do this is part of the project).
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5. Determine whether the anomaly as you define it has an economic and stat-
istical effect and explain a trading strategy. To this end, provide summary
statistics and performance of the returns on the portfolios so generated and
the trading strategy. Test the economic and statistical significance of the re-
turns on your trading strategy using the Fama/French Developed 3 Factors
(see Fama & Kenneth, 1993; Fama & French, 2012), Fama/French Developed 3
Factors augmented by the Developed Momentum Factor (see Carhart, 1997),
and Fama/French Developed 5 Factors (see Fama & French, 2016; Fama &
French, 2017) rate of returns data. This data can also be downloaded from
Professor Kenneth French’s Data library.
6. Do some sub-period analysis and check whether the anomaly is constant
through your sample.
7. Repeat the analysis with market friction costs (e.g., transaction costs, etc) of
5 basis points (0.05%) for each trade.
8. Comment on your results with regards to previous literature and the develop-
ment of a trading strategy.
This project will involve a literature review, data collection, and empirical invest-
igation. The project involves the work of all group members and should be seen
as a hands-on example of using materials from Quantitative Finance module. The
submission should include example(s) of your calculations plus commentary. The
text of the assignment must be typed (12-point font, one-half spacing, one-inch
margins), although any graphs etc. can be drawn, following an academic-style writ-
ing: Introduction, Literature Review, Data and Methodology, Results and Analysis,
Conclusions, and Limitations of your study. The maximum word length is 3000
excluding bibliography and appendices. Late submission will not be accepted (see
below about late submission). This is a group project worth 40% of the final grade
and every member of the group is to contribute. All group members receive the
same grade. Each group should assign a coordinator who is to manage the group.
Please include the following statement on the first page of your assignment that
should be signed by all group members: “We declare that all materials included in
this project are the result of our own work and we have all contributed to the project.”
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Submission of Report
The group project write-up should be submitted electronically to Turnitin on Moodle
as a PDF file, by the deadline on Monday, 26th November 2021 at 23:59 CET. Please
do not email the project write-up directly to the teaching team. Only your data,
calculations, computer programs, excel calculations, etc should be emailed directly
to your respective professors. This is mandatory to enable us understand what is in
your written report.
Late Submission
Late submissions receive a penalty of an absolute 25% per week, so make sure you
leave loads of time to upload your report. The penalty works as follows: any report
not uploaded on time receives an automatic 25% reduction (e.g. 60% falls to 35%),
and every Monday after that, another 25% is deducted.
Plagiarism
Please ensure the submission is entirely your own work as plagiarism is taken very
seriously. The Turnitin program on Moodle checks your project for any plagiarised
content.
References
Asness, C. S., Moskowitz, T. J. & Pedersen, L. H. (2013), ‘Value and momentum every-
where’, The Journal of Finance 68(3), 929–985.
Carhart, M. M. (1997), ‘On persistence in mutual fund performance’, The Journal of
Finance 52(1), 57–82.
Fama, E. F. & French, K. R. (1992), ‘The cross-section of expected stock returns’, The
Journal of Finance 47(2), 427–465.
Fama, E. F. & French, K. R. (2012), ‘Size, value, and momentum in international stock
returns’, Journal of Financial Economics 105(3), 457–472.
Fama, E. F. & French, K. R. (2016), ‘Dissecting anomalies with a five-factor model’, The
Review of Financial Studies 29(1), 69–103.
Fama, E. F. & French, K. R. (2017), ‘International tests of a five-factor asset pricing
model’, Journal of Financial Economics 123(3), 441–463.
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Fama, E. F. & Kenneth, R. (1993), ‘French, 1993, common risk factors in the returns on
stocks and bonds’, Journal of Financial Economics 33(1), 3–56.
Harvey, C. R., Liu, Y. & Zhu, H. (2016), ‘. . . and the cross-section of expected returns’,
The Review of Financial Studies 29(1), 5–68.
Hou, K., Xue, C. & Zhang, L. (2020), ‘Replicating anomalies’, The Review of Financial
Studies 33(5), 2019–2133.
Jegadeesh, N. & Titman, S. (1993), ‘Returns to buying winners and selling losers: Implic-
ations for stock market efficiency’, The Journal of Finance 48(1), 65–91.
McLean, R. D. & Pontiff, J. (2016), ‘Does academic research destroy stock return predict-
ability?’, The Journal of Finance 71(1), 5–32.
Rosenberg, B., Reid, K. & Lanstein, R. (1985), ‘Persuasive evidence of market inefficiency’,
The Journal of Portfolio Management 11(3), 9–16.
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