Machine Learning for Forecasting Excess Stock Returns – The Five-Year-View
Ioannis Kyriakou (),
Parastoo Mousavi (),
Jens Perch Nielsen () and
Michael Scholz ()
Additional contact information
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
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
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:grz:wpaper:2019-06
Ordering information: This working paper can be ordered from
Access Statistics for this paper
More papers in Graz Economics Papers from University of Graz, Department of Economics University of Graz, Universitaetsstr. 15/F4, 8010 Graz, Austria. Contact information at EDIRC.
Bibliographic data for series maintained by Michael Scholz ().