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Study of the Forbes 500

Group 8 Masatoshi Hirokawa, Han Liu, Christian Mundo, Ashley Arlotti, Jingyu Nie, and Aygul Nagaeva. Study of the Forbes 500. What?. The Forbes 500 includes the top companies of each major industry

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Study of the Forbes 500

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  1. Group 8 Masatoshi Hirokawa, Han Liu, Christian Mundo, Ashley Arlotti, Jingyu Nie, and Aygul Nagaeva Study of the Forbes 500

  2. What? • The Forbes 500 includes the top companies of each major industry • Data includes sales, profits, market-value, assets, cash-flow, and employees of each company • Industries included are energy, finance, transportation, hi-tech, manufacturing, communication, medical, and retail

  3. Why? • Understanding the correlation of variables in an industry gives insight to: • Health • Growth • Value

  4. How? • To understand the correlation we used Ordinary Least Squares to create regression equations • If values were questionable further analysis is done through White’s test (Testing for heteroskedasticity)

  5. Variables Used • Facts about companies selected from the Forbes 500 list for 1986. This is a 1/10 systematic sample from the alphabetical list of companies. (Data found at: http://lib.stat.cmu.edu/DASL/Datafiles/Companies.html) • Sales: Amount of sales (in millions) • Assets: Amount of assets (in millions) • Market_Value: Market Value of the company (in millions) • Profits: Profits (in millions) • Cash_Flow: Cash Flow (in millions) • Employees: Number of employees (in thousands) • Sector: Type of market the company is associated with

  6. Total Sales by Sector

  7. Sector Proportions

  8. Regression Before Grouping • Dummies: • Other • Energy • Finance • Transportation • Hi-Tech • Manufacturing • Communication • Medical • Retail

  9. Dropping Variables • All remaining variables are significant • Only dum3 is left, representing the financial sector

  10. Adding Profits • Until this point we have left profits out of the regression because of its relationship with sales

  11. Regression of Profits vs Sales • This is the regression performed with profits as the dependent variable and Sales as the independent variable

  12. Profits vs Sales

  13. White’s Test Since the Profits vs Sales regression had a negative correlation coefficient we did extra analysis with the White’s Test to find heteroskedasticity in the residuals

  14. Regression Before Grouping • Dummies: • Other • Energy • Finance • Transportation • Hi-Tech • Manufacturing • Communication • Medical • Retail

  15. Wald’s Test In order to group all of the Dummy variables we used a Wald’s Test from the equation: SALES = C(1)*MARKET + C(2)*EMPLOYEE + C(3)*CASHFLOW + C(4)*ASSETS + C(5)*ENERGYD2 + C(6)*FINANCED3 + C(7)*TRANSPORTATIOND4 + C(8)*HITECHD5 + C(9)*MANUFACTURINGD6 + C(10)*COMMUCATIOND7 + C(11)*MEDICALD8 + C(12)*RETAILD9

  16. Regression After Grouping • Dummy Negative includes finance (dum3), hi-tech (dum5), Communication (dum7), and Medical (dum8) • Dummy Positive includes energy (dum2), transportation (dum4), manufacturing (dum6), and retail (dum9)

  17. Final Equation • SALES = 61.25792336(EMPLOYEE) + 0.222356458(ASSETS) + 1.405446264(CASHFLOW) - 897.3202179(DUMNEG )+ 710.3874293(DUMPOS) • Every 1000 employees generates about 61 million dollars in sales • Every dollar in assets gives .22 in sales • Every dollar of cash flow correlates to 1.4 in sales • Depending on the sector, there is a negative or positive effect on sales

  18. Conclusion • The only insignificant variable in determining the number of sales for a company or industry is market-value • Profits is negatively correlated with number of sales which could be because of its heteroskedastic error or increase in production cost • Grouping dummy variables for sector together helped to make a more significant regression.

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