Just number 1 please. Thankyou Regression analysis can be used to test whether the market efficiently uses information in valuingstocks. For concreteness, let return be the total return from holding a firm's stock over the four-year period from the end of 1990 to the end of 1994. The efficient markets hypothesis saysthat these returns should not be systematically related to information known in 1990. If firmcharacteristics known at the beginning of the period help to predict stock returns, then we coulduse this information in choosing stocks.1For 1990, let dkr be a firm's debt to capital ratio, let eps denote the earnings per share, letnetinc denote net income, and let salary denote total compensation for the CEO.1. Using relevant data, the following equation was estimated:return = -14.37 + .321dkr + .043eps – .0051etinc + .0035salary(6.89) (.201)(.078)(.0047)(.0022)n = 142 R2 = .0395Test whether the explanatory variables are jointly significant at the 5% level. Is anyexplanatory variable individually significant at the 5% significance level?2. Now, reestimate the model using the log of netinc and salary:return = -36.30 + .327dkr + .069eps – 4.74log(netinc) + 7.24log(salary)(.080)(39.37) (.203)(3.39)(6.31)n = 142 R2 = .0330Do any of your conclusions from part (1.) change?3. In this sample, some firms have zero debt and others have negative earnings. Should wetry to use log(dkr) or log(eps) in the model to see if these improve the fit? Explain.4. Overall, is the evidence for predictability of stock returns strong or weak?

Regression analysis can be used to test whether the market efficiently uses information in valuing stocks. For concreteness, let return be the total return from holding a firm's stock over the four- year period from the end of 1990 to the end of 1994. The efficient markets hypothesis says that these returns should not be systematically related to information known in 1990. If firm characteristics known at the beginning of the period help to predict stock returns, then we could use this information in choosing stocks. 1

Regression analysis can be used to test whether the market efficiently uses information in valuing stocks. For concreteness, let return be the total return from holding a firm's stock over the four- year period from the end of 1990 to the end of 1994. The efficient markets hypothesis says that these returns should not be systematically related to information known in 1990. If firm characteristics known at the beginning of the period help to predict stock returns, then we could use this information in choosing stocks. 1

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Question

Just number 1 please. Thankyou

Regression analysis can be used to test whether the market efficiently uses information in valuing
stocks. For concreteness, let return be the total return from holding a firm's stock over the four-
year period from the end of 1990 to the end of 1994. The efficient markets hypothesis says
that these returns should not be systematically related to information known in 1990. If firm
characteristics known at the beginning of the period help to predict stock returns, then we could
use this information in choosing stocks.
1
For 1990, let dkr be a firm's debt to capital ratio, let eps denote the earnings per share, let
netinc denote net income, and let salary denote total compensation for the CEO.
1. Using relevant data, the following equation was estimated:
return = -14.37 + .321dkr + .043eps – .0051etinc + .0035salary
(6.89) (.201)
(.078)
(.0047)
(.0022)
n = 142 R2 = .0395
Test whether the explanatory variables are jointly significant at the 5% level. Is any
explanatory variable individually significant at the 5% significance level?
2. Now, reestimate the model using the log of netinc and salary:
return = -36.30 + .327dkr + .069eps – 4.74log(netinc) + 7.24log(salary)
(.080)
(39.37) (.203)
(3.39)
(6.31)
n = 142 R2 = .0330
Do any of your conclusions from part (1.) change?
3. In this sample, some firms have zero debt and others have negative earnings. Should we
try to use log(dkr) or log(eps) in the model to see if these improve the fit? Explain.
4. Overall, is the evidence for predictability of stock returns strong or weak?

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