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Article

Return Forecasting by Quantile Regression

Lawrence Pohlman and Lingjie Ma
The Journal of Investing Winter 2010, 19 (4) 116-121; DOI: https://doi.org/10.3905/joi.2010.19.4.116
Lawrence Pohlman
is chief investment officer of quantitative strategies at BNP Paribas IP in Boston, MA.
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  • For correspondence: lawrence.pohlman@bnpparibas.com
Lingjie Ma
is research manager at BMO Asset Management in Boston, MA.
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  • For correspondence: lingjie.ma@bmo.com
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Abstract

A typical quantitative approach for analyzing and forecasting equity returns is to build a model based on a set of factors and then estimate the model based on a set of data and some type of least squares procedure. However, as the data in equity markets are usually far from well behaved and some standard statistical assumptions do not hold, this procedure can miss significant relationships. This article uses the quantile regression technique to reveal effects that are missed by OLS. The empirical results using S&P 500 Index data show dramatic improvement in performance using QR forecasts.

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The Journal of Investing: 19 (4)
The Journal of Investing
Vol. 19, Issue 4
Winter 2010
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Return Forecasting by Quantile Regression
Lawrence Pohlman, Lingjie Ma
The Journal of Investing Nov 2010, 19 (4) 116-121; DOI: 10.3905/joi.2010.19.4.116

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Return Forecasting by Quantile Regression
Lawrence Pohlman, Lingjie Ma
The Journal of Investing Nov 2010, 19 (4) 116-121; DOI: 10.3905/joi.2010.19.4.116
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  • Article
    • Abstract
    • WHAT IS QUANTILE REGRESSION?
    • EXAMPLES OF QUANTILE REGRESSION
    • WHAT VALUE OF t?
    • MULTI-FACTOR MODEL CASE
    • QUINTILE SPREADS
    • OPTIMIZED PORTFOLIO PERFORMANCE
    • SUMMARY
    • ENDNOTES
    • REFERENCES
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