Ordinary Least Squares regression ignores both heteroscedasticity and cross-correlations of abnormal returns; therefore, tests of regression coefficients are weak and biased. A portfolio ordinary least squares (POLS) regression accounts for correlations and ensures unbiasedness of tests, but does not improve their power. The authors 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.

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.

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.

This paper examines the relationship between the level of short interest and stock returns in the Nasdaq market from June 1988 through December 1994. We find that heavily shorted firms experience significant negative abnormal returns ranging from −0.76 to −1.13 percent per month after controlling for the market, size, book‐to‐market, and momentum factors. These negative returns increase with the level of short interest, indicating that a higher level of short interest is a stronger bearish signal. We find that heavily shorted firms are more likely to be delisted compared to their size, book‐to‐market, and momentum matched control firms.