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Machine Learning for Forecasting Excess Stock Returns – The Five-Year-View

Ioannis Kyriakou (), Parastoo Mousavi (), Jens Perch Nielsen () and Michael Scholz ()
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Ioannis Kyriakou: Cass Business School, City, University of London, UK
Parastoo Mousavi: Cass Business School, City, University of London, UK
Jens Perch Nielsen: Cass Business School, City, University of London, UK
Michael Scholz: University of Graz, Austria

No 2019-06, Graz Economics Papers from University of Graz, Department of Economics

Abstract: In this paper, we apply machine learning 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. In particular, we adopt and implement a fully nonparametric smoother with the covariates and the smoothing parameter chosen by cross-validation. We find that for both one-year and five-year returns, the term spread is, overall, the most powerful predictive variable for excess stock returns. Differently combined covariates can then achieve higher predictability for different forecast horizons. Nevertheless, the set of earnings-by-price and term spread predictors under the inflation benchmark strikes the right balance between the one-year and five-year horizon.

Keywords: Benchmark; Cross-validation; Prediction; Stock returns; Long-term forecasts; Overlapping returns; Autocorrelation (search for similar items in EconPapers)
JEL-codes: C14 C53 C58 G17 G22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-ecm, nep-fmk and nep-for
Date: 2019-08
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