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Return forecasts and optimal portfolio construction: a quantile regression approach

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  • Lingjie Ma
  • Larry Pohlman

Abstract

In finance there is growing interest in quantile regression with the particular focus on value at risk and copula models. In this paper, we first present a general interpretation of quantile regression in the context of financial markets. We then explore the full distributional impact of factors on returns of securities and find that factor effects vary substantially across quantiles of returns. Utilizing distributional information from quantile regression models, we propose two general methods for return forecasting and portfolio construction. We show that under mild conditions these new methods provide more accurate forecasts and potentially higher value-added portfolios than the classical conditional mean method.

Suggested Citation

  • Lingjie Ma & Larry Pohlman, 2008. "Return forecasts and optimal portfolio construction: a quantile regression approach," The European Journal of Finance, Taylor & Francis Journals, vol. 14(5), pages 409-425.
    • Handle: RePEc:taf:eurjfi:v:14:y:2008:i:5:p:409-425
    DOI: 10.1080/13518470802042369
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    References listed on IDEAS

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    1. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
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    7. Gebka, Bartosz & Wohar, Mark E., 2019. "Stock return distribution and predictability: Evidence from over a century of daily data on the DJIA index," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 1-25.
    8. Wan-Ni Lai & Claire Y. T. Chen & Edward W. Sun, 2022. "Risk factor extraction with quantile regression method," Annals of Operations Research, Springer, vol. 316(2), pages 1543-1572, September.
    9. Al-Nasseri, Alya & Menla Ali, Faek & Tucker, Allan, 2021. "Investor sentiment and the dispersion of stock returns: Evidence based on the social network of investors," International Review of Financial Analysis, Elsevier, vol. 78(C).
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    12. Zhao, Yixiu & Upreti, Vineet & Cai, Yuzhi, 2021. "Stock returns, quantile autocorrelation, and volatility forecasting," International Review of Financial Analysis, Elsevier, vol. 73(C).
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    14. Gebka, Bartosz & Wohar, Mark E., 2018. "The predictive power of the yield spread for future economic expansions: Evidence from a new approach," Economic Modelling, Elsevier, vol. 75(C), pages 181-195.
    15. Chiuling Lu & Ann Yang & Jui-Feng Huang, 2015. "Bankruptcy predictions for U.S. air carrier operations: a study of financial data," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(3), pages 574-589, July.
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    17. Zhu, Hui-Ming & Li, ZhaoLai & You, WanHai & Zeng, Zhaofa, 2015. "Revisiting the asymmetric dynamic dependence of stock returns: Evidence from a quantile autoregression model," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 142-153.
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