“Cost of Capital” in Residual Income for Performance Evaluation

2002 ◽  
Vol 77 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Peter O. Christensen ◽  
Gerald A. Feltham ◽  
Martin G. H. Wu

We consider a setting in which a firm uses residual income to motivate a manager's investment decision. Textbooks often recommend adjusting the residual income capital charge for market risk, but not for firmspecific risk. We demonstrate two basic flaws in this recommendation. First, the capital charge should not be adjusted for market risk. Charging a market risk premium results in “double” counting because a risk-averse manager will personally consider this risk. Second, while investors can avoid firm-specific risk through diversification, a manager cannot. If the manager faces significant firm-specific risk at the time he makes his investment decision, then it is optimal to charge him less than the riskless return so as to partially offset his reluctance to undertake risky investments. On the other hand, the manager will vary his investment decisions with the pre-decision information he receives, which accentuates his compensation risk, and the firm must compensate him for bearing this additional risk. Hence, if the manager will receive relatively precise pre-decision information, then it is optimal to charge him more than the riskless return to reduce the variability of his investment decisions.

2015 ◽  
Author(s):  
Pablo Fernandez ◽  
Alberto Ortiz Pizarro ◽  
Isabel Fernnndez Accn

2011 ◽  
Vol 47 (1) ◽  
pp. 115-135 ◽  
Author(s):  
Mariano González ◽  
Juan Nave ◽  
Gonzalo Rubio

AbstractThis paper explores the cross-sectional variation of expected returns for a large cross section of industry and size/book-to-market portfolios. We employ mixed data sampling (MIDAS) to estimate a portfolio’s conditional beta with the market and with alternative risk factors and innovations to well-known macroeconomic variables. The market risk premium is positive and significant, and the result is robust to alternative asset pricing specifications and model misspecification. However, the traditional 2-pass ordinary least squares (OLS) cross-sectional regressions produce an estimate of the market risk premium that is negative, and significantly different from 0. Using alternative procedures, we compare both beta estimators. We conclude that beta estimates under MIDAS present lower mean absolute forecasting errors and generate better out-of-sample performance of the optimized portfolios relative to OLS betas.


2014 ◽  
Vol 23 (2) ◽  
pp. 51-58 ◽  
Author(s):  
Austin Murphy ◽  
Liang Fu ◽  
Terry Benzschawel

1985 ◽  
Vol 40 (4) ◽  
pp. 1251-1253 ◽  
Author(s):  
R. STEPHEN SEARS ◽  
K. C. JOHN WEI

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