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Quantile Regression As A Tool For Portfolio Investment Decisions During Times Of Financial Distress

Author

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  • D. E. ALLEN

    (School of Accounting, Finance and Economics, Edith Cowan University, Australia)

  • R. J. POWELL

    (School of Accounting, Finance and Economics, Edith Cowan University, Australia)

  • A. K. SINGH

    (School of Accounting, Finance and Economics, Edith Cowan University, Australia)

Abstract

The worldwide impact of the Global Financial Crisis (GFC) on stock markets, investors and fund managers has lead to a renewed interest in appropriate tools for robust risk management. Quantile regression is a powerful technique and deserves the interest of financial decision makers given its remarkable capabilities for capturing and explaining the behavior of financial return series across a distribution more effectively than ordinary least squares regression methods which are the standard tool. In this paper, we present quantile regression estimation as an attractive additional investment tool, which is more effective than Ordinary Least Squares (OLS) in analyzing information across the quantiles of a distribution. This translates into the more accurate calibration of asset pricing models and subsequent informational gains in portfolio formation. We present empirical evidence of the superior capabilities of quantile regression based techniques as applied across the quantiles of return distributions to derive information for portfolio formation. We show, via stocks in Dow Jones Industrial Index, that at times of financial shocks, such as the GFC, a portfolio of stocks formed using quantile regression in the context of the Fama–French three-factor model, performs better than the one formed using traditional OLS.

Suggested Citation

  • D. E. Allen & R. J. Powell & A. K. Singh, 2011. "Quantile Regression As A Tool For Portfolio Investment Decisions During Times Of Financial Distress," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 1-19.
  • Handle: RePEc:wsi:afexxx:v:06:y:2011:i:01:n:s2010495211500035
    DOI: 10.1142/S2010495211500035
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    References listed on IDEAS

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    1. -, 1992. "Jamaica Project Data Bank: ILPES," Sede de la CEPAL en Santiago (Estudios e Investigaciones) 30005, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
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    3. Michelle L. Barnes & Anthony W. Hughes, 2002. "A quantile regression analysis of the cross section of stock market returns," Working Papers 02-2, Federal Reserve Bank of Boston.
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    Citations

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    Cited by:

    1. David E. Allen & Michael McAleer & Abhay K. Singh, 2019. "Daily market news sentiment and stock prices," Applied Economics, Taylor & Francis Journals, vol. 51(30), pages 3212-3235, June.
    2. Ruchika Sehgal & Aparna Mehra, 2023. "Quantile Regression Based Enhanced Indexing with Portfolio Rebalancing," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(3), pages 721-742, September.
    3. Jaeyoung Cheong & Heejoon Lee & Minjung Kang, 2021. "Stock Index Prediction using Cointegration test and Quantile Loss," Papers 2109.15045, arXiv.org.

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    More about this item

    Keywords

    Factor models; portfolio optimization; quantile regression; G11; G12; C21;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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