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Forecasting benchmarks of long-term stock returns via machine learning

Author

Listed:
  • Ioannis Kyriakou

    (University of London)

  • Parastoo Mousavi

    (University of London)

  • Jens Perch Nielsen

    (University of London)

  • Michael Scholz

    (University of Graz)

Abstract

Recent advances in pension product development seem to favour alternatives to the risk free asset often used in the financial theory as a performance standard for measuring the value generated by an investment or a reference point for determining the value of a financial instrument. To this end, in this paper, we apply the simplest machine learning technique, namely, a fully nonparametric smoother with the covariates and the smoothing parameter chosen by cross-validation to forecast stock returns in excess of different benchmarks, including the short-term interest rate, long-term interest rate, earnings-by-price ratio, and the inflation. We find that, net-of-inflation, the combined earnings-by-price and long-short rate spread form our best-performing two-dimensional set of predictors for future annual stock returns. This is a crucial conclusion for actuarial applications that aim to provide real-income forecasts for pensioners.

Suggested Citation

  • Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Forecasting benchmarks of long-term stock returns via machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 221-240, February.
    • Handle: RePEc:spr:annopr:v:297:y:2021:i:1:d:10.1007_s10479-019-03338-4
    DOI: 10.1007/s10479-019-03338-4
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    Cited by:

    1. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Short-Term Exuberance and Long-Term Stability: A Simultaneous Optimization of Stock Return Predictions for Short and Long Horizons," Mathematics, MDPI, vol. 9(6), pages 1-19, March.
    2. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
    3. Erdinc Akyildirim & Aurelio F. Bariviera & Duc Khuong Nguyen & Ahmet Sensoy, 2022. "Forecasting high-frequency stock returns: a comparison of alternative methods," Annals of Operations Research, Springer, vol. 313(2), pages 639-690, June.
    4. Parastoo Mousavi, 2021. "Debt-by-Price Ratio, End-of-Year Economic Growth, and Long-Term Prediction of Stock Returns," Mathematics, MDPI, vol. 9(13), pages 1-18, July.

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