Forecasting Stock Returns: A Predictor-Constrained Approach

42 Pages Posted: 18 Oct 2017 Last revised: 21 Jun 2019

See all articles by Zhiyuan Pan

Zhiyuan Pan

Southwestern University of Finance and Economics (SWUFE) - Institute of Chinese Financial Studies (ICFS)

Davide Pettenuzzo

Brandeis University - International Business School

Yudong Wang

Shanghai Jiao Tong University (SJTU)

Date Written: June 20, 2019

Abstract

We develop a novel method to impose constraints on univariate predictive regressions of stock returns. Unlike the previous approaches in the literature, we implement our constraints directly on the predictor, setting it to zero whenever its value falls below the variable's past 12-month high. Empirically, we find that relative to standard unconstrained predictive regressions, our approach leads to significantly larger forecasting gains, both in statistical and economic terms. We also show how a simple equal-weighted combination of the constrained forecasts leads to further improvements in forecast accuracy, with predictions that are more precise than those obtained either using the Campbell and Thompson (2008) or Pettenuzzo, Timmermann, and Valkanov (2014) methods. Subsample analysis and a large battery of robustness checks confirm that these findings are robust to the presence of model instabilities and structural breaks.

Keywords: Equity premium, Predictive regressions, Predictor constraints, 12-month high, Model combinations

JEL Classification: C11, C22, G11, G12

Suggested Citation

Pan, Zhiyuan and Pettenuzzo, Davide and Wang, Yudong, Forecasting Stock Returns: A Predictor-Constrained Approach (June 20, 2019). Available at SSRN: https://ssrn.com/abstract=3054652 or http://dx.doi.org/10.2139/ssrn.3054652

Zhiyuan Pan

Southwestern University of Finance and Economics (SWUFE) - Institute of Chinese Financial Studies (ICFS) ( email )

Chengdu
China

Davide Pettenuzzo

Brandeis University - International Business School ( email )

Mailstop 32
Waltham, MA 02454-9110
United States

Yudong Wang (Contact Author)

Shanghai Jiao Tong University (SJTU)

KoGuan Law School
Shanghai 200030, Shanghai 200052
China

Register to save articles to
your library

Register

Paper statistics

Downloads
162
Abstract Views
645
rank
182,027
PlumX Metrics