More Powerful Portfolio Approaches to Regressing Abnormal Returns on Firm-Specific Variables for Cross-Sectional Studies




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    • Faculty of Business Administration, University of Windsor, and J. L. Kellogg Graduate School of Management, Northwestern University, respectively. We appreciate the suggestions and comments of Yash Aneja, Carla Hayn, Bob Korajczyk, Bill Salatka, participants at the accounting and finance group doctoral seminar at the University of Kansas, and the anonymous referee. We are especially grateful to the referee whose insight and suggestions improved the paper significantly. Chandra's research was supported by the Social Sciences and Humanities Research Council of Canada.


OLS regression ignores both heteroscedasticity and cross-correlations of abnormal returns; therefore, tests of regression coefficients are weak and biased. A Portfolio OLS (POLS) regression accounts for correlations and ensures unbiasedness of tests, but does not improve their power. We propose Portfolio Weighted Least Squares (PWLS) and Portfolio Constant Correlation Model (PCCM) regressions to improve the power. Both utilize the heteroscedasticity of abnormal returns in estimating the coefficients; PWLS ignores the correlations, while PCCM uses intra-and inter-industry correlations. Simulation results show that both lead to more powerful tests of regression coefficients than POLS.