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- Title
Forecasting benchmarks of long-term stock returns via machine learning.
- Authors
Kyriakou, Ioannis; Mousavi, Parastoo; Nielsen, Jens Perch; Scholz, Michael
- 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.
- Subjects
MACHINE learning; FINANCIAL instruments; FINANCIAL performance; STOCKS (Finance); ABNORMAL returns; STOCK prices
- Publication
Annals of Operations Research, 2021, Vol 297, Issue 1/2, p221
- ISSN
0254-5330
- Publication type
Article
- DOI
10.1007/s10479-019-03338-4