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The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests

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  • Gupta, Rangan
  • Pierdzioch, Christian
  • Vivian, Andrew J.
  • Wohar, Mark E.

Abstract

We contribute to research on the predictability of stock returns in two ways. First, we use quantile random forests to study the predictive value of various consumption-based and income-based inequality measures across the quantiles of the conditional distribution of stock returns. Second, we examine whether the inequality measures, measured at a quarterly frequency, have out-of-sample predictive value for stock returns at three different forecast horizons. Our results suggest that the inequality measures have predictive value for stock returns in sample, but do not systematically predict stock returns out of sample.

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  • Gupta, Rangan & Pierdzioch, Christian & Vivian, Andrew J. & Wohar, Mark E., 2019. "The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests," Finance Research Letters, Elsevier, vol. 29(C), pages 315-322.
  • Handle: RePEc:eee:finlet:v:29:y:2019:i:c:p:315-322
    DOI: 10.1016/j.frl.2018.08.013
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    Cited by:

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    2. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2022. "Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data," Energies, MDPI, vol. 15(22), pages 1-26, November.
    3. Riza Demirer & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2022. "Risk aversion and the predictability of crude oil market volatility: A forecasting experiment with random forests," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(8), pages 1755-1767, August.
    4. Hassani, Hossein & Yeganegi, Mohammad Reza & Gupta, Rangan, 2019. "Does inequality really matter in forecasting real housing returns of the United Kingdom?," International Economics, Elsevier, vol. 159(C), pages 18-25.
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    6. Fazlollah Soleymani & Houman Masnavi & Stanford Shateyi, 2020. "Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique," Mathematics, MDPI, vol. 9(1), pages 1-15, December.
    7. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2023. "Climate Risks and Forecasting Stock Market Returns in Advanced Economies over a Century," Mathematics, MDPI, vol. 11(9), pages 1-21, April.
    8. Taussig, Roi D., 2021. "Competition risk and expected stock returns," Finance Research Letters, Elsevier, vol. 41(C).
    9. Afees A. Salisu & Rangan Gupta, 2021. "Commodity Prices and Forecastability of South African Stock Returns Over a Century: Sentiments versus Fundamentals," Working Papers 202144, University of Pretoria, Department of Economics.
    10. Hossein Hassani & Mohammad Reza Yeganegi & Rangan Gupta & Riza Demirer, 2022. "Forecasting stock market (realized) volatility in the United Kingdom: Is there a role of inequality?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2146-2152, April.
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    13. Chia-Cheng Chen & Yisheng Liu & Ting-Hsin Hsu, 2019. "An Analysis on Investment Performance of Machine Learning: An Empirical Examination on Taiwan Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 9(4), pages 1-10.

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    More about this item

    Keywords

    Stock returns; Predictability; Inequality measures; Quantile random forests;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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