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The Optimal Use of Return Predictability: An Empirical Study

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  • Abhyankar, Abhay
  • Basu, Devraj
  • Stremme, Alexander

Abstract

In this paper we study the economic value and statistical significance of asset return predictability, based on a wide range of commonly used predictive variables. We assess the performance of dynamic, unconditionally efficient strategies, first studied by Hansen and Richard (1987) and Ferson and Siegel (2001), using a test that has both an intuitive economic interpretation and known statistical properties. We find that using the lagged term spread, credit spread, and inflation significantly improves the risk-return trade-off. Our strategies consistently outperform efficient buy-and-hold strategies, both in and out of sample, and they also incur lower transactions costs than traditional conditionally efficient strategies.

Suggested Citation

  • Abhyankar, Abhay & Basu, Devraj & Stremme, Alexander, 2012. "The Optimal Use of Return Predictability: An Empirical Study," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 47(5), pages 973-1001, October.
    • Handle: RePEc:cup:jfinqa:v:47:y:2012:i:05:p:973-1001_00
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    Cited by:

    1. Cotter, John & Eyiah-Donkor, Emmanuel & Potì, Valerio, 2017. "Predictability and diversification benefits of investing in commodity and currency futures," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 52-66.
    2. Vigo Pereira, Caio, 2021. "Portfolio efficiency with high-dimensional data as conditioning information," International Review of Financial Analysis, Elsevier, vol. 77(C).
    3. Chiang, I-Hsuan Ethan, 2015. "Modern portfolio management with conditioning information," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 114-134.
    4. Fletcher, Jonathan & Basu, Devraj, 2016. "An examination of the benefits of dynamic trading strategies in U.K. closed-end funds," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 109-118.
    5. Massimo Guidolin & Manuela Pedio, 2020. "Distilling Large Information Sets to Forecast Commodity Returns: Automatic Variable Selection or HiddenMarkov Models?," BAFFI CAREFIN Working Papers 20140, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    6. Massimo Guidolin & Manuela Pedio, 2022. "Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns," Forecasting, MDPI, vol. 4(1), pages 1-32, February.

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