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Forecasting benchmarks of long-term stock returns via machine learning

Kyriakou, I. ORCID: 0000-0001-9592-596X, Mousavi, P., Nielsen, J. P. and Scholz, M. (2019). Forecasting benchmarks of long-term stock returns via machine learning. Annals of Operations Research, doi: 10.1007/s10479-019-03338-4

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.

Publication Type: Article
Additional Information: © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Publisher Keywords: Benchmark, Cross-validation, Prediction, Stock returns
Subjects: H Social Sciences > HG Finance
Departments: Cass Business School > Actuarial Science & Insurance
URI: http://openaccess.city.ac.uk/id/eprint/22578
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