Data Analytics Research Project – Executive Compensation Model


University of Maryland - Baltimore

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Data Analytics Research Project – Executive Compensation Model Status: Required Due Date: May 1, 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 company’s 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 • 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. • • 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. 𝐓𝐎𝐓𝐀𝐋_𝐂𝐔𝐑𝐑 = 𝜷𝟎 + 𝜷𝟏 𝑨𝑮𝑬 + 𝜷𝟐 𝑮𝑬𝑵𝑫𝑬𝑹 + 𝜷𝟑 𝐒𝐇𝐑𝐎𝐖𝐍𝐓𝐎𝐓 + 𝜷𝟒 𝐄𝐗𝐄𝐂𝐑𝐀𝐍𝐊 + 𝛆 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 𝜀 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. ( Citation instructions To cite this data, use the following format: Wharton Research Data Services. " Compustat Execucomp data (Compustat report)", 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 1 Quantitative Analysis Project – Data Analytics 2 INTRODUCTION There is a new real estate company in Bowie Maryland known as ABC Reality that was founded by Mary Clay. Her goal is to determine what the relationship is between the price of houses that are currently listed and the number of stories, bedrooms, bathrooms in the house, and the age of the house. The data that Mary has acquired to begin this research is a huge set of data that was collected in 2017. This source of data was obtained from a 2017 American House Survey. To do this research, Mary will need to develop a hedonic pricing model with the help of SPSS. Visualizations will also be created from her data through Tableau. PART I: Data Visualizations (Tableau) The first graph above shows that as the number of stories within a house increase, the average market value increases as well. So, the more stories that are in a house the higher the price will be to buy it. The second graph shows that as the age of the house increase, the average market value decreases. So, the older the house gets the lower the price is to buy it. Also, this second graph shows that basically after about ten years there is a huge drop in price for the 3 houses. This could possibly be that it is outdated after that amount of years, so it loses its value and not a lot of buyers are willing to buy a house that is outdated. An article states that “A few changes, such as replacing the fixtures, choosing modern paint colors, and installing newer appliances, are often enough to generate interest at a better sales price. Otherwise, buyers will generally not agree to pay top-of-market for an outdated home, even if it is newer and clean (Weintraub, 2019).” The first graph above shows that as the number of bedrooms increase, the average market value increases as well. So basically, the more bedrooms there are in a house the higher it will be in price. The second graph above shows that as the number of bathrooms increase, the average market value increases as well. This is the same concept as the bedrooms because the more bathrooms that are in a house the higher it will be in price. This second graph also shows that there is a decrease in price after 6 bathrooms up until about 9 and then it starts to increase again. This could be because the average maximum for bathrooms within a decent size house could be six or the maximum amount a person would want. But once you go pass that, that many 4 bathrooms are not needed unless you’re living in a mansion or a really huge house. An article from the year 2012 states that “Real estate brokers who cater to the moneyed say their clients typically want homes that have at least two bathrooms for every bedroom (Beale, 2012).” Also, “For those who can afford it, an abundance of bathrooms provides convenience and privacy for both guest and residents (Beale, 2012).” The graph above is data collected from the year 2017 so that could also be why there is that increase again from 9 up until 13 bathrooms. PART II: Predictive Analysis – Multiple Regression Model (SPSS) Coefficientsa Standardized Unstandardized Coefficients Model 1 B (Constant) Std. Error 491428.123 11019.667 Stories 13107.885 2155.972 Age -3591.484 Bedrooms Bathrooms Coefficients Beta t Sig. 44.596 .000 .031 6.080 .000 109.236 -.175 -32.878 .000 -9881.423 3152.644 -.020 -3.134 .002 9621.542 2334.918 .028 4.121 .000 a. Dependent Variable: MarketVal The estimated equation for this specific regression would be as follows: ̂ 𝑀𝑎𝑟𝑘𝑒𝑡𝑉𝑎𝑙 = 491428.123 + 13107.885𝑆𝑡𝑜𝑟𝑖𝑒𝑠 − 3591.484𝐴𝑔𝑒 − 9881.423𝐵𝑒𝑑𝑟𝑜𝑜𝑚𝑠 + 9621.542𝐵𝑎𝑡ℎ𝑟𝑜𝑜𝑚𝑠 From the regression results shown above, it basically explains that all the including variables such as stories, age, bedrooms, and bathrooms are significant determinants of the market value because all of the p-values are less than .05. Also, all of the variables have the expected sign except for the number of bedrooms. It should be positive for bedrooms because the higher the market value or the price of the house, the more bedrooms there are within that house. 5 It is possible that the data may not be good because it is showing that bedrooms have the wrong sign. Because of this we will try running another regression without bedrooms included. Model Summary Model R .185a 1 R Square Adjusted R Std. Error of the Square Estimate .034 .034 520122.46263 a. Predictors: (Constant), Bathrooms, Stories, Age, Bedrooms Above is the Model Summary which is a test for the Goodness of Fit. It is shown that R Square is equal to .034 which is also 3.4%. So, 3.4% of the variability in the Market Value is explained by the regression equation based on stories, age, bedrooms, and bathrooms. This percentage is actually bad because a lot of the variables should have been included, which is why it is low. ANOVAa Model 1 Sum of Squares Regression Residual df Mean Square 3721494325257 4 9303735813143 38.000 4.500 1048888742731 38772 1086103685984 343.911 Sig. .000b 270527376130. 9086.000 Total F 173 38776 4824.000 a. Dependent Variable: MarketVal b. Predictors: (Constant), Bathrooms, Stories, Age, Bedrooms The ANOVA chart above is a test for the overall significance of the model. Because the estimated equation for the regression was obtained, it is required that the estimated model is tested to see if it is significant or not. This is done by testing the null hypothesis against the alternative as shown below: 6 𝐻0 : 𝛽1 = 𝛽2 = 𝛽3 = 𝛽4 = 0 𝐻𝐴 : 𝛽1 ≠ 𝛽2 ≠ 𝛽3 ≠ 𝛽4 ≠ 0 The chart above also reports the p-value which is shown as ‘Sig’. Since, 0.000 < 0.05 this means the overall model is strongly significant. So, all the independent variables (stories, age, bedrooms, bathrooms) jointly determine the dependent variable (MarketVal). Below is another regression that was ran without the variable bedrooms. Model Summary Model R 1 .184a Adjusted R Std. Error of Square the Estimate R Square .034 .034 520181.64438 a. Predictors: (Constant), Bathrooms, Stories, Age ANOVAa Sum of Model 1 Squares df Regression 36949176699 3 Mean Square F 1231639223 455.170 8792.000 Residual .000b 32930.670 10491545092 38773 2705889431 846032.000 Total Sig. 52.349 10861036859 38776 844824.000 a. Dependent Variable: MarketVal b. Predictors: (Constant), Bathrooms, Stories, Age Coefficientsa Model 1 Unstandardized Standardized Coefficients Coefficients B (Constant) 474911.988 Std. Error 9679.228 Beta t 49.065 Sig. .000 7 Stories 14595.384 2103.329 .035 6.939 .000 Age -3626.263 108.683 -.177 -33.365 .000 5157.643 1850.483 .015 2.787 .005 Bathrooms a. Dependent Variable: MarketVal As you can see, R Squared provided in the Model Summary is still equal to 3.4%. So, the percentage is still bad due to all the variables not being included. Then there is the ANOVA chart which still shows that the p-value is 0.000 < 0.05. So, the model is still strongly significant overall. Lastly, there is the coefficients chart where the estimated equation for this specific regression was formulated. It is basically the same thing as the last one, but the numbers are slightly different and all the signs are as expected. After running this second regression and seeing that it is basically the same as the last one, this just means that the data given is not good. CONCLUSION This research was done so that Mary Clay can determine the relationship between the listing price for houses and the number of stories, bedrooms, bathrooms in the house, and the age of the house. What she found using the hedonic pricing model and her data is that the more stories, bedrooms, and bathrooms a house has, the higher the price is to buy it. Also, the older the house is the lower the price is to buy it. With that knowledge Mary will now know how to make a sound business decision and what most of her clients will be looking for when it comes to her new real estate company. 8 References Beale, L. (2012, March 2). Wealthy Home Buyers Demand Bathrooms. Retrieved from LA Times: Weintraub, E. (2019, July 14). Tips for Buying a House that Needs Work. Retrieved from The Balance: ...
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