Optimal asset allocation and nonlinear return predictability from the dividend-price ratio

Fabrizio Ghezzi (), Anindo Sarkar (), Thomas Quistgaard Pedersen () and Allan Timmermann ()
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Fabrizio Ghezzi: UCSD
Anindo Sarkar: UCSD
Thomas Quistgaard Pedersen: Aarhus University
Allan Timmermann: UCSD

Annals of Operations Research, 2025, vol. 346, issue 1, No 23, 415-445

Abstract: Abstract We study non-linear predictability of stock returns arising from the dividend-price ratio and its implications for asset allocation decisions. Using data from five countries — U.S., U.K., France, Germany and Japan — we find empirical evidence supporting non-linear and time-varying models for the equity risk premium. Building on this, we examine several model specifications that can account for non-linear return predictability, including Markov switching models, regression trees, random forests and neural networks. Although in-sample return regressions and portfolio allocation results support the use of non-linear predictability models, the out-of-sample evidence is notably weaker, highlighting the difficulty in exploiting non-linear predictability in real time.

Keywords: Asset allocation; Dynamics and predictability of stock returns; Dividend-price ratio dynamics; Nonlinear return predictability; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-024-06332-7

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