The Predictive Ability Of Stock Market Factors

Abstract

Purpose This paper asks whether a range of stock market factors contain information that is useful to investors by generating a trading rule based on one-step-ahead forecasts from rolling and recursive regressions. Design/methodology/approach Using USA data across 3256 firms, we estimate stock returns on a range of factors using both fixed-effects panel and individual regressions and, using rolling and recursive approaches, generate time-varying coefficients. Subsequently, we generate one-step ahead forecasts for expected returns, simulate a trading strategy and compare its performance with realised returns. Findings Results from the panel and individual firm regressions show that an extended Fama-French five-factor model that includes momentum, reversal and quality factors outperform other models. Moreover, rolling based regressions outperform recursive ones in forecasting returns. Research limitations/implications Our results support notable time-variation in the coefficients on each factor, while suggesting that more distant observations, inherent in recursive regressions, do not improve predictive power over more recent observations. Results support the ability of market factors to improve forecast performance over a buy-and-hold strategy. Practical implications The results presented here will be of interest to both academics in understanding the dynamics of expected stock returns and investors who seek to improve portfolio performance through understanding which factors determine stock return movement. Originality/value We investigate the ability of risk factors to provide accurate forecasts and thus have economic value to investors. We conducted a series of moving and expanding window regressions to trace the dynamic movements of the stock returns average response to explanatory factors. We use the time-varying parameters to generate one-step-ahead forecasts of expected returns and simulate a trading strategy.