Study of the Forbes 500

Group 8Masatoshi Hirokawa, Han Liu,

Christian Mundo, Ashley Arlotti,

Jingyu Nie, and Aygul Nagaeva

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

Why?

Understanding the correlation of variables in an industry gives insight to:HealthGrowthValue

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)

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

Total Sales by Sector

Sector Proportions

Regression Before Grouping

Dummies:1.Other2.Energy3.Finance4.Transportation5.Hi-Tech6.Manufacturing7.Communication8.Medical 9.Retail

Dropping Variables

All remaining variables are significant

Only dum3 is left, representing the financial sector

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

Regression of Profits vs Sales

•This is the regression performed with profits as the dependent variable and Sales as the independent variable

Profits vs Sales

0

20000

40000

60000

-2000 0 2000 4000 6000 8000

PROFIT S

SA

LE

S

White’s TestSince 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

0

1000000

2000000

3000000

4000000

5000000

0 20000 40000 60000

SALES

RE

SID

SQ

Regression Before Grouping

Dummies:1.Other2.Energy3.Finance4.Transportation5.Hi-Tech6.Manufacturing7.Communication8.Medical 9.Retail

Wald’s TestIn 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

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)

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

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.