Forecasting benchmarks of long-term stock returns via machine learning

Ioannis Kyriakou (), Parastoo Mousavi (), Jens Perch Nielsen () and Michael Scholz ()
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Ioannis Kyriakou: University of London
Parastoo Mousavi: University of London
Jens Perch Nielsen: University of London
Michael Scholz: University of Graz

Annals of Operations Research, 2021, vol. 297, issue 1, No 10, 240 pages

Abstract: 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.

Keywords: Benchmark; Cross-validation; Prediction; Stock returns (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10479-019-03338-4

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