Predicting Stock Market Returns with Aggregate Discretionary Accruals

53 Pages Posted: 22 Jun 2006

See all articles by Qiang Kang

Qiang Kang

Florida International University (FIU) - Department of Finance

Qiao Liu

Peking University - Guanghua School of Management

Rong Qi

ING Aeltus Asset Management

Multiple version iconThere are 2 versions of this paper

Date Written: June 2006

Abstract

We document that the value-weighted aggregate discretionary accruals have significant power in predicting the one-year-ahead stock market returns between 1965 and 2004. The predictive relation is stable and robust to different ways to measure market returns and discretionary accruals as well as to the inclusion of other known return predictors. The value-weighted aggregate discretionary accruals are positively related to future stock market returns and negatively correlated with contemporaneous market returns. Our extensive analysis favors the managerial equity market timing story and suggests that managers of large firms have stronger market timing ability than managers of small firms.

Keywords: aggregate discretionary accruals, time-varying risk premium, predictive regressions, managerial market timing

JEL Classification: G1, M4

Suggested Citation

Kang, Qiang and Liu, Qiao and Qi, Rong, Predicting Stock Market Returns with Aggregate Discretionary Accruals (June 2006). EFA 2006 Zurich Meetings Paper, Available at SSRN: https://ssrn.com/abstract=890685 or http://dx.doi.org/10.2139/ssrn.890685

Qiang Kang (Contact Author)

Florida International University (FIU) - Department of Finance ( email )

University Park
11200 SW 8th Street
Miami, FL 33199
United States

Qiao Liu

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

Rong Qi

ING Aeltus Asset Management ( email )

10 State House Square
Hartford, CT 06457
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
634
Abstract Views
5,716
Rank
31,551
PlumX Metrics