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C31RM: Research Methods, 2020 - 2021.
COURSEWORK: QUANTITATIVE ELEMENT (60%)
The Research Methods course assessment for the quantitative element accounts for
60% of the course grade and is based on an individual empirical project. This empirical
project consists of two parts:
 Part I requires estimation of Ordinary Least Squares (OLS) regression models and
interpretation of the results;
 Part II is a set of tests for Weak Form Efficiency and their interpretations.
These two empirical topics require understanding of material covered in the
Quantitative lectures and tutorials. You will also need to use SPSS for the tasks
required for this project. Further details about the write up and presentation of this
individual project are at the end of this document.
You are required to submit your report on Wednesday 31st March, 2021 at 12.00
Edinburgh time (15.00 Dubai local time).
Part I: Regression Analysis
The main objective of this component of the individual project is to 1) run the regression
using OLS and 2) to write up a concise report, discussing and analyzing your results,
based on suitable results tables and graphs.
Step 1: Obtain the Data: The data is available in the Assessments tab on the course
Vision page under the file name 'CEO Data'
Step 2: You are required to run the following regression model using OLS:
= + 1 + 2 + 3σi + 4 + 5
+ 6 +
where: is the log salary for CEO i;
is the return on assets % for firm i;
is measured by the log of firm i’s total assets;
σi is the volatility measured by the daily return standard deviation (%);
is the age of the CEO for firm i;

is a dummy variable, = 1 if CEO is female, 0 otherwise;
is the percentage of independent director (%).
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Step 3: Empirical Discussions
1) Report the results in a simple table – which should include the estimated
coefficients, their standard errors, the value of 2R , adj- 2R , and any other statistics
you think are appropriate.
2) Discuss your findings. For example, what this equation means and how it can be
justified? What are the interpretations of the estimated coefficients? Are the
estimated coefficients statistically significant? How well does the model describe
the relationship between the variables? Do the assumptions underlying the model
hold?
3) Discuss your findings in relation to the relevant literature on CEO performance.
4) Optional Opportunity for Individual Initiative:
In this part of your Report you have the opportunity (if you wish to use it - not
compulsory) to discuss other potential additional variables that may be
useful in explaining CEO performance.
Part II: Tests for Weak Form Efficiency
In this part of the project you will need to perform a few tests for weak form efficiency
on selected stocks and/or market indices, and discuss your empirical findings.
Step 1: Obtain the Data
You need to obtain daily data for 2 - 3 commodity prices or stock prices over a span of
at least two years up until the closet day of your last birthday1. You may obtain the data
from DataStream, EIA, Worldbank, IMF, or Yahoo Finance2.
You can either compute the simple return as follows:
=
− −1
−1
× 100
where is return at time , is the price at time t and −1 is the price at time − 1.
Alternatively, you can make use of log returns.
You must include a screenshot on the FIRST PAGE ONLY of the spreadsheet with
all the data in the Appendix.
1 For example, if your birthday is 5th November, then you can have your data from 5th November 2018 to 5th
November 2020.
2 Be careful with Yahoo Finance, you may need to sort the data from the earliest to latest order. They have
data ordered from the latest to the earliest.
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Step 2: Tests of Weak Form Efficiency
You should read one of the “overview” chapters detailed below and the research articles
provided. These will give you guidance about how to interpret and comment on the results
of the weak-form efficiency tests as applied to your data.
Textbook Overviews:
 Bodie, Zvi, Alex Kane and Alan Markus, Investments, Chapter 11 in the 8th Edition on “The
Efficient Market Hypothesis” or the same chapter in other Editions.
 Elton, Gruber, Brown, Goetzmann: Modern Portfolio Theory and Investment Analysis, 6th
Edition Chapter 17 on Efficient Markets.
 Campbell, John, Andrew Lo and A Craig MacKinlay, The Econometrics of Financial Markets,
Princeton University Press, 1997 (Chapters 1 and 2)
Journal Articles:
 Cont, Rama, 2001, Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues,
Quantitative Finance, 1, 223–236.
 Jacobs, Bruce and Kenneth Levy, 1988, Calendar Anomalies: Abnormal Returns at Calendar
Turning Points, Financial Analysts Journal, 28-39.
1) Descriptive Statistics and return distributions
Begin by analyzing the data using the summary statistics (e.g., mean, median, max, min,
skewness, kurtosis etc.) in a table. Provide a few (may be 1 or 2) plots of time series of
returns of the data and a histogram of the return distribution for your stocks and indices.
Using appropriate wording you should comment on the statistical and economic (if any)
2) Autoregressive (AR) Model
You can test to see if the return data in your sample follow a random walk using the AR
(1) model:
= + −1 +
where the dependent variable is the return for the time t, and the independent variable
−1 is the return lagged one period for time − 1.
You can create a one-period lag of return either manually in excel or in SPSS (For
instance, by using the LAG function under the Transform>Compute variables menu).
You should report and discuss your results.
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3) Day of the Week Effect
In this part, you will test and see if the data shows any seasonal effects over days of
the week. Before you attempt this part, you should carefully read the relevant material:
 Koop, Gary, Analysis of Financial Data Chapter 7
 Brooks, Chris, Introductory Econometrics for Finance, 2nd Edition; Chapter 9, Sections 9.2-9.3
pages 454-462.
You should run the following regression:
tt uDDDDDR 
Mon 1,t Tue 2,t W ed 3,t Thu 4,t Fri 5,t
where: tR = return at time t

Otherwise
Monday
D t,
0
1
1
t,D2 , t,D3 , t,D4 and t,D5 are dummy variables for Tuesdays, Wednesdays,
Thursdays and Fridays, respectively.
Note: There is no constant in the above regression; if you want to include a constant you
can have only 4 dummy variables. Again, you should report and comment on your
findings.
4) Optional Opportunity for Individual Initiative
In this part of your Report you have the opportunity (if you wish to use it - not
compulsory) to use the data on your sample stocks for either a) implementing a test
for weak form efficiency that you have read about and can use on your own – different
from the tests you have already done above; or b) try and think of something that will
efficiency. For example, compute autocorrelation coefficients for your chosen stocks;
use additional data to test “Month-of-the-Year” seasonal effect.
For ideas on what you could do you need to have a look at the references given and
also search for further articles if necessary.
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Guidelines For Writing The Project Report
a) The marking criteria are as follows:
 40%: For neat and logical presentation, crisp and precise language and
You will lose marks for providing only raw computer output, untidy
presentation and a sketchy discussion of the results, and for non-compliance
with the style guidelines.
 40%: For the Empirical Analysis – especially the discussion of the test results
and importantly any “economic” interpretation of the results. For tips on how
to describe and discuss the empirical results you get, look at the template papers
accompanying the Exercises.
 10%: For overall conciseness, cogent and careful analysis of results and logical
flow of the entire report.
 10%: For work that shows initiative and understanding of the material “on your
own” that will add value to your work and demonstrate that you have an
appreciation of the topic. However, this is all subject to the allocated word limit!
b) The length of the Report should be approx. 2,000 words [+ or - 10% fluctuations
permitted]
The following items are excluded from the word count:
 Tables, figures and graphs
 References list
 Appendices
You must use at least 1.5 point line spacing and a font size not smaller than 12 pts. It
is your decision as to how to balance the word count between Parts I and II of the
assignment.
c) Some further notes on the format:
 Your report must not contain any raw output from Excel, SPSS or any other
package.
 When reporting numerical results round up the numbers to three or four decimal
places. You should check how published papers report numerical results and
follow a consistent and clear style. You should not report numbers like
0.00001243; this is not very useful to a reader.