What Matters When? Time-Varying Sparsity in Expected Returns

2019 ◽  
Author(s):  
Daniele Bianchi ◽  
Matthias Büchner ◽  
Andrea Tamoni
Keyword(s):  
Expected Returns ◽  
Time Varying ◽  
What Matters

scholarly journals End-of-the-year economic growth and time-varying expected returns

2015 ◽  
Vol 115 (1) ◽  
pp. 136-154 ◽  
Author(s):  
Stig V. Møller ◽  
Jesper Rangvid
Keyword(s):  
Economic Growth ◽  
Expected Returns ◽  
Time Varying

The predictive ability of stock market factors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Mohammed Elgammal ◽  
Fatma Ehab Ahmed ◽  
David Gordon McMillan
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Five Factor Model ◽  
Trading Strategy ◽  
Expected Returns ◽  
Time Varying ◽  
Content Type ◽  
Varying Coefficients ◽  
One Step ◽  
Market Factors

Purpose This paper aims to ask 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 3,256 firms, the authors estimate stock returns on a range of factors using both fixed-effects panel and individual regressions. The authors use rolling and recursive approaches to generate time-varying coefficients. Subsequently, the authors 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 The results support notable time-variation in the coefficients on each factor, whilst 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 highlighting which factors determine stock return movement. Originality/value The authors investigate the ability of risk factors to provide accurate forecasts and thus have economic value to investors. The authors conducted a series of moving and expanding window regressions to trace the dynamic movements of the stock returns average response to explanatory factors. The authors use the time-varying parameters to generate one-step-ahead forecasts of expected returns and simulate a trading strategy.

Sign in / Sign up

Export Citation Format

Share Document