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Do industry returns predict the stock market? A reprise using the random forest

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  • Ciner, Cetin

Abstract

The prior work reports conflicting evidence on the information content of industry returns for the market index return. We reexamine the out of sample predictive ability of industry returns by considering several relatively advanced methods from the statistical learning literature. We show that when the random forest method, which accounts for both linear and nonlinear dynamics, is used for regression, industry returns indeed contain significant out of sample forecasting power for the market index return. Moreover, our analysis also presents evidence for lead-lag relations among individual industry returns. The reported findings are consistent with the implications of the gradual diffusion of information hypothesis.

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  • Ciner, Cetin, 2019. "Do industry returns predict the stock market? A reprise using the random forest," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 152-158.
    • Handle: RePEc:eee:quaeco:v:72:y:2019:i:c:p:152-158
    DOI: 10.1016/j.qref.2018.11.001
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    Cited by:

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    2. Chen, Tzu-Ying & Tsai, An-Mei & Tzeng, Larry Y., 2022. "Revisiting almost marginal conditional stochastic dominance," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 260-269.
    3. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Nawaf Almaskati, 2022. "Machine learning in finance: Major applications, issues, metrics, and future trends," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-32, September.
    5. Yan, Wan-Lin, 2023. "Stock index futures price prediction using feature selection and deep learning," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    6. Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2020. "Forecasting Realized Stock-Market Volatility: Do Industry Returns have Predictive Value?," Working Papers 2020107, University of Pretoria, Department of Economics.
    7. 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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