Alternative Profitability Measures and Cross Section of Expected Stock Returns: International Evidence

37 Pages Posted: 18 May 2017 Last revised: 4 Jun 2020

See all articles by Nusret Cakici

Nusret Cakici

Fordham University

Sris Chatterjee

Fordham University - Gabelli School of Business

Yi Tang

Fordham University - Gabelli School of Business

Lin Tong

Fordham University - Finance Area

Date Written: May 16, 2017

Abstract

This paper provides an extensive international analysis of the cross-sectional return predictive power of a variety of firm-level profitability measures, calculated from different combinations of measures of earnings and scaling variables. We show that this cross-sectional predictive relation is more pronounced when profit is measured by gross profit and when profits are scaled by enterprise value or market value of equity. Our findings support the hypotheses that the predictive power of “profits-to-market price” factor is partly attributable to stock mispricing arising from systematic behavioral biases and partly to the choice of a “clean” measure of earnings.

Keywords: Profitability, enterprise value, behavioral finance, international asset pricing

JEL Classification: G11, G12, G15

Suggested Citation

Cakici, Nusret and Chatterjee, Sris and Tang, Yi and Tong, Lin, Alternative Profitability Measures and Cross Section of Expected Stock Returns: International Evidence (May 16, 2017). Available at SSRN: https://ssrn.com/abstract=2969687 or http://dx.doi.org/10.2139/ssrn.2969687

Nusret Cakici (Contact Author)

Fordham University ( email )

Fordham University
Graduate School of Business
New York, NY 10023
United States
2126366776 (Phone)

Sris Chatterjee

Fordham University - Gabelli School of Business ( email )

113 West 60th Street
New York, NY 10023
United States

Yi Tang

Fordham University - Gabelli School of Business ( email )

113 West 60th Street
New York, NY 10023
United States

Lin Tong

Fordham University - Finance Area ( email )

33 West 60th Street
New York, NY 10023
United States

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