University of Edinburgh Business School Quantitative Research Methods in Finance Individual research project

***Please read this brief carefully***

The individual research project contributes 50% to the total mark. In this project, you must collect the data for the industries assigned to you, conduct empirical analysis in Stata, and report, interpret and discuss the estimation results in relation to the research questions.

You are required to submit two files: (1) a final report (in MS word format) and (2) a zip file containing the data files and a single do file.¹

Name the zip file “ExamID_QRMF_Files.zip” and the do file as “ExamID_QRMF_Code.do”. For instance, if your exam ID is B12345678, the zip file should be named as “B12345678_QRMF_Files.zip” and the do file as “B12345678_QRMF_Code.do”. The do file should be self-explanatory as to which lines display the output for a particular question. The original raw data files plus the Stata do file should be included in the zip file.

An example of code organization in a do file is as follows (note that “*” is the sign for “comment-out” in Stata):

***************************

*Question 1 – Summary Statistics

*************************** Code here

****************************

*Question 2 – Correlation Analysis

**************************** Code here

In the final report, when reporting the estimation results, you must use screenshots or images of the direct Stata outputs. Each image (table) should show the Stata command and its output. These tables are not included in your word count. An example is shown below:

The word count for the final report is max 1,500 words.² The word count excludes all words in tables (except Table 6, which is included in the word count), figures, the Appendix (if any) and the references section. State the word count after answering each question.

In the final report, you should use Times New Roman 11 point font on A4 pages with 2.54 cm margins on each side.

Please make sure that there are no names or other identifying information in any of the files submitted so that anonymity can be maintained during the marking.

The deadline for submitting your assignment is 2pm 1^st of May. The coursework submission should be made electronically to Learn via the submission portal.

The assessed project must be entirely your own work. Severe penalties will be imposed on students who are found guilty of plagiarism. You may find the suggested readings helpful in understanding the context and background of the research questions tested. Please remember to reference your sources using an appropriate citation system (such as the Harvard or APA style). The report will lose marks for poor referencing. More information about referencing can be found here.

Your data allocation

Based on the last digit in the student number, each student has been assigned four industries to analyse and a corresponding dependent variable (Tobin’s Q or ROA). The industries are defined under the Fama-French 12-industry classification. The definitions of the industries can be found here. Hence, your sample will consist of all available firms in the four industries assigned to you. The Fama-French 12-industry variable in the provided dataset is FF12.

Table 1A. Assigned Fama-French industries

Fama-French 12 industries (the industry number in parentheses)	Student numbers ending in
Non Durable (1) + Durable (2) + Shops (9) + Finance (11)	1
Durable (2) + Manufacturing (3) + Shops (9) + Finance (11)	2
Manufacturing (3) + Energy (4) + Shops (9) + Finance (11)	3
Energy (4) + Chemicals (5) + Shops (9) + Finance (11)	4
Chemicals (5) + Business Equipment (6) + Shops (9) + Finance (11)	5
Business Equipment (6) + Telecom (7) + Shops (9) + Finance (11)	6
Telecom (7) + Utilities (8) + Shops (9) + Finance (11)	7
Utilities (8) + Shops (9) + Health care (10) + Finance (11)	8
Shops (9) + Health care (10) + Finance (11) + Others (12)	9
Others (12) + Non Durable (1) + Shops (9) + Finance (11)	0

Table 1B. Assigned dependent variable

Dependent variables                                                                        Student numbers ending in Performance                                                                                     1, 3, 5, 7, 9

You will lose marks for going beyond the word limit.

SCENARIO

ESG practices have received increasing attention from policymakers and practitioners around the world over the last ten years. There are both arguments in favour and against a positive effect of ESG practices on firm outcomes such as performance, risk, layout policies, gender pay-gap, etc. Some empirical studies find a positive effect (Gillan et al. 2010; Lins et al. 2017, Liang and Renneboog 2017; Iliev and Roth 2020), while others find a negative or no relationship (Di Giuli and Kostovetsky 2014; Buchanan et al. 2018; Hsu et al. 2018).

In this project, you will address a research question examining whether ESG has an effect on one of the following variables: (1) a firm’s stock market performance OR (b) a firm’s accounting performance.

Part A.

REQUIRED:

Using appropriate data sources (e.g. Compustat, CRSP, Refinitiv, Sustainalytics and BoardEx), download data (see above) for your assigned industries for the period 2010-2019.

Answer the following questions for your four assigned industries.

1. Summary Statistics: Obtain a final sample by excluding any missing observations in all the variables you will use in the regression analysis. Compute any appropriate ratios and perform the functional form transformations of the variables that you will use in the subsequent statistical analysis. Winsorize the variables at the 1^st and 99^th percentiles for variables taking on both positive and negative values, and at the 99^th percentile for variables that only take on positive values. Report two tables showing the summary statistics before and after winsorization for all the variables – raw and transformed, including the number of observations, mean, standard deviation, median, minimum and maximum.

(Label this table: Table 1a – Summary Statistics before Winsorization and Table 1b – Summary Statistics after Winsorization)

[10 marks; max. 0 words]

• Pairwise Correlations: Report a correlation matrix for all the variables you will use in the regression analysis (after winsorization).

(Label this table: Table 2 – Correlation Matrix)

[5 marks; max. 0 words]

• You want to assess whether firms with better ESG practices have an effect on performance. Based on a strong literature review and data availability, choose a variable that proxies for ESG practices and two relevant control variables. One of the control variables should be used as a dummy variable, so make a sensible choice. Control for firm size and add time dummies. Using all these variables run a firm-fixed effects regression and include this table in your project. Label this table: Table 3: Fixed Effects. This table does not count towards the word count. Make sure all the variables are fully defined at the end or beginning of the table in the form of table notes.

[10 marks; max. 0 words]

• Run the same regression as in task 3 above but report clustered standard errors. Label this table: Table 4: Fixed Effects with Clustered Std Errors. This table does not count towards the word count. Clearly explain your rationale for choosing this clustering and write down the STATA command used to make these calculations.

[10 marks; max. 60 words]

• Add an interaction term involving your ESG proxy and your chosen dummy variable to the regression reported in Table 4. Label this table: Table 5: Interaction Effects. This table does not count towards the word count. Carry out an analysis using the margins command showing the marginal effect of your dummy variable when the ESG variable is at its 25^th and 75^th percentiles. Are these marginal effect statistically significant (answer Yes or No for each effect)? Illustrate the economic magnitude of the variables involving your interaction. Make sure to include relevant tables and plots that may add value to your answer.

[10 marks; max. 200 words]

• Compare the results of the models in Table 3, Table 4 and Table 5: the magnitudes and significance of the coefficients. Come up with at least two plausible explanations for the differences between the estimates involving ESG in tables 4 and 5.

[10 marks; max. 200 words]

• Give an example of an unobserved omitted variable that could lead to your ESG estimated coefficient being biased. Spell out in detail the mechanism through which the bias works.

[10 marks; max. 200 words]

Part B

In your regression analysis you are using a cleaned reduced dataset and the reader is therefore not aware of the steps undertaken to reach this final sample. In this part of the project you will assess whether the sample you used in your regressions is representative of the population and how the undertaken data management decisions (e.g. dropping missing observations) that were taken could potentially affect the inference you carried out in the first part of the project.

• Complete the following table

[10 marks; max. 0 words]

Sample Requirements	Unique Firms	Firm-years
Number of raw annual firm observations, 2010-2019
Less observations:
No dependent variable information
No ESG information
Missing control variable X1
Missing Control Variable X2
Final Sample

• Based on the table above, explain whether the sample you used in Part A is likely to lead to unbiased estimations. Justify your answer.

[15 marks; max. 200 words]

• List your references (at least 5 references). These have to be different to the ones listed in the section Scenario.

[5 marks; max. 240 words]

• Add a table of variable definitions. Label this table: Table 6: Variable Definitions. Make sure that every variable used in every table and regression is clearly defined. Label the variables consistently throughout the project.

[5 marks; max. 400 words]

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