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Predicting European stock returns using machine learning

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  • Antonio Marsi

    (University of Bologna)

Abstract

This paper examines the predictability of equity returns in the European stock market through the use of several machine learning techniques. Specifically, we focus on the monthly returns of equities included in the EURO STOXX 50 index from 1994 to 2018, with a large set of stock-level characteristics interacted with macroeconomic variables as predictors. Unlike prior studies that used US data and larger samples, we find that linear methods perform better than highly non-linear ones in terms of predictability. However, the overall predictive ability of machine learning algorithms remains relatively low, indicating that equity returns in our sample are highly unpredictable, in line with the efficient market hypothesis. Our findings suggest that while machine learning may offer some predictive power, it is not a panacea for predicting equity returns in the European stock market.

Suggested Citation

  • Antonio Marsi, 2023. "Predicting European stock returns using machine learning," SN Business & Economics, Springer, vol. 3(7), pages 1-25, July.
    • Handle: RePEc:spr:snbeco:v:3:y:2023:i:7:d:10.1007_s43546-023-00487-4
    DOI: 10.1007/s43546-023-00487-4
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    More about this item

    Keywords

    Machine learning; Stock returns predictability; Forecasting; European stock market;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G0 - Financial Economics - - General
    • G1 - Financial Economics - - General Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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