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

Author

Listed:
  • Fabrizio Ghezzi

    (UCSD)

  • Anindo Sarkar

    (UCSD)

  • Thomas Quistgaard Pedersen

    (Aarhus University)

  • Allan Timmermann

    (UCSD
    UCSD)

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.

Suggested Citation

  • Fabrizio Ghezzi & Anindo Sarkar & Thomas Quistgaard Pedersen & Allan Timmermann, 2025. "Optimal asset allocation and nonlinear return predictability from the dividend-price ratio," Annals of Operations Research, Springer, vol. 346(1), pages 415-445, March.
    • Handle: RePEc:spr:annopr:v:346:y:2025:i:1:d:10.1007_s10479-024-06332-7
    DOI: 10.1007/s10479-024-06332-7
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