The Perils of Relying on Return Data When Testing Asset Pricing Models

Abstract

Asset pricing models are almost always tested using stock returns over multiple time periods, and the returns of portfolios over the investment horizon determined using the arithmetic average of these portfolio returns. The arithmetic average returns of portfolios selected using the model’s parameters are calculated and compared. However, investors’ returns are derived from changes in the value of their portfolios. This paper shows how the use of arithmetic returns creates large biases in the magnitude and statistical significance of asset pricing models’ outcomes. It argues only evaluations using the values of portfolios produce reliable results. The identified bias is created because a positive return and its equal but negative return, represent different sized price movements, and this becomes obscured when returns are analysed and averaged over multiple periods. Most existing pricing models are potentially invalid because of the biases generated by the methodology used in their development.