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Forecasting Stock Returns: A Predictor-Constrained Approach

Davide Pettenuzzo (), Zhiyuan Pan () and Yudong Wang
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Zhiyuan Pan: Southwestern University of Finance and Economics, Institute of Chinese Financial Studies

No 116R, Working Papers from Brandeis University, Department of Economics and International Businesss School

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 at 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 tosignificantly 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; 24-month high and low; Model combinations (search for similar items in EconPapers)
JEL-codes: C11 C22 G11 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
Date: 2017-10, Revised 2018-02
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http://www.brandeis.edu/economics/RePEc/brd/doc/Brandeis_WP116R.pdf First version, 2017 (application/pdf)

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