Data Analytics Research Project Executive Compensation Model

Bowie State University April 9, 2020
Department of Accounting, Finance, and Economics
ECON 483-101 QUANTITATIVE METHODS FOR DECISION MAKING Spring 2020
Instructor: Dr. Thaddee Badibanga
Data Analytics Research Project Executive Compensation Model
Status: Required
Due Date: May 7, 2020
Project Goals
The main goal of this project is to help students to apply quantitative analysis techniques including
statistical methods particularly predictive analytics to an empirical analyze and predict the
relationship between the executive compensation and a set of covariates that affect such
compensation. Each student is expected to analyze and predict the relationship, draw conclusions,
make policy recommendations and compose a written report.
Learning objectives
Upon completing this research project, the student will be able to:
Describe the relationship between executive compensation and factors that affect it;
Develop a regression model to analyze the relationship between executive compensation
and a set of covariates;
Collect relevant data and apply data analytics to describe empirically the relationship;
Use predetermined values of the covariates to predict the executive compensation in the
future;
Draw informed conclusions.
Problem Statement
The data used to analyze this data analytics project is obtained from the Warton Research Data
Services (WRDS). The Compustat Execucomp database provided executive compensation data
collected directly from each companys annual proxy (DEF14A SEC form). Detailed information
on salary, bonus, options and stock awards, non-equity incentive plans, pensions and other
compensation items are available on annual basis since 1992 (see AnnComp Summary
compensation data + other). The data in each table contains additional header information on
company IDs and individual identification.
The data to be used is provided in the attached excel format.
Resources
The following are resources you can use to improve knowledge about regression analysis.
You can obtain additional knowledge on statistical analysis and the use of the SPSS
software by completing the Statistics 101 on IBM Cognitive class using the following link
https://cognitiveclass.ai/courses/statistics-101/
Simply create an account using your BSU email and complete the course. You will learn,
obtain a certificate. Your certificate of completion must be attached to your report.
https://www.youtube.com/watch?reload=9&v=fO7g0pnWaRA
https://www.tableau.com/learn/training
Assignment
Task 1 Data Cleaning
The data is not provided in a suitable state. It is necessary to get the data into a proper form that
supports your analysis, that is, you are expected to make it ready for the analysis by cleaning and
reconciling it using Excel or Tableau. Each student is expected to use data of a single state.
Task 2 Data Visualization
You are expected to create various visualizations using Excel or Tableau to detect the
relationship between the executive compensation and each of its covariate.
Task 3 Predictive Analytics
Develop and estimate a multiple regression model to determine the relationship between
executive compensation and its covariates. The equation to be estimated is specified below.
?????_???? = ?? + ????? + ???????? + ??????????? + ?????????? + ?
where:
TOTAL_CURR is Total Current Compensation (Salary + Bonus);
AGE is executive’s age;
GENDER is gender;
EXECRANK is current rank by salary + bonus; and
? is the random error.
Assess the quality of your results of estimation in term of the fitness of the regression model (i.e.
R-squared and standard deviation) and the hypothesis test on each estimated coefficient (using Fstat or p-value or t-stat). Also, interpret the results of estimation.
Task 4 Report
Write a paper to present the results of your analysis and make policy recommendations for the
determination of the salaries and bonuses of the executive personnel.
Each project must include at least 5 pages excluding title page, cover page and references. It will
be written using the following guidelines and contents:
Title page (Include project title and student name) (5%)
Introduction: Problem of the proposed study, purpose and justification of the study (15%)
Data analytics various calculations and estimations (45%)
Interpretation of results (15%)
Findings and conclusion. (10%)
Appendices: Tables and Figures. (5%)
References (5%)
The project will be written using the APA style. (https://apastyle.apa.org/index)
Citation instructions
To cite this data, use the following format:
Wharton Research Data Services. ” Compustat Execucomp data (Compustat report)”
wrds.wharton.upenn.edu, accessed 04/08/2020.
Variable Name Data Type Variable Description
AGE NUM AGE — Executive’s Age
EXECRANK NUM Current Rank by Salary + Bonus
EXECRANKANN NUM EXECRANKANN — Executive Rank by Salary + Bonus
GENDER CHAR Gender
GVKEY CHAR Company ID Number
JOINED_CO NUM Date Joined Company
LEFTCO NUM Date Left Company
PAGE NUM Present Age
SALARY NUM SALARY — Salary ($)
SHROWN_TOT NUM SHROWN_TOT — Shares Owned – As Reported
STATE CHAR State
TOTAL_CURR NUM
TOTAL_CURR — Total Current Compensation (Salary +
Bonus)
YEAR NUM YEAR — Fiscal Year