Use the following output from a market model regression of the weekly percentage returns on the AIG commodities index on a market index to answer questions a. - f.   a. What is the formula for the Commodities Index' characteristic line? b. You forecast a market return of 1.0% for next week. What is next week's expected return for the commodities index? c. What is the correlation between the return on the commodities index and the return on the market Index? d. How much of the variation in the commodities index's returns are explained by the model? e. Based on these regression results, the commodities index would be considered what kind of an investment? f. Does this regression have much explanatory power?  Why or why not?

Cornerstones of Financial Accounting
4th Edition
ISBN:9781337690881
Author:Jay Rich, Jeff Jones
Publisher:Jay Rich, Jeff Jones
Chapter2: The Accounting Information System
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Use the following output from a market model regression of the weekly percentage returns on the AIG commodities index on a market index to answer questions a. - f.

 

a. What is the formula for the Commodities Index' characteristic line?

b. You forecast a market return of 1.0% for next week. What is next week's expected return for the commodities index?

c. What is the correlation between the return on the commodities index and the return on the market Index?

d. How much of the variation in the commodities index's returns are explained by the model?

e. Based on these regression results, the commodities index would be considered what kind of an investment?

f. Does this regression have much explanatory power?  Why or why not?

 

 

SUMMARY OUTPUT
Regression Statistics
Multiple R
0.20
R Square
0.04
Adjusted R Square
0.02
Standard Error
2.53
Observations
51.00
ANOVA
df
1.00
Significance F
MS
Regression
13.60
13.60
2.12
0.15
Residual
49.00
313.90
6.41
Total
50.00
327.50
Coefficients
Standard Error
t Stat
P-value
Intercept
0.13
0.36
0.37
0.71
Index
0.35
0.24
1.46
0.15
Transcribed Image Text:SUMMARY OUTPUT Regression Statistics Multiple R 0.20 R Square 0.04 Adjusted R Square 0.02 Standard Error 2.53 Observations 51.00 ANOVA df 1.00 Significance F MS Regression 13.60 13.60 2.12 0.15 Residual 49.00 313.90 6.41 Total 50.00 327.50 Coefficients Standard Error t Stat P-value Intercept 0.13 0.36 0.37 0.71 Index 0.35 0.24 1.46 0.15
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