Diverging Roads: Theory-based vs. machine learning-implied stock risk premia

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Zitierfähiger Link (URI): http://hdl.handle.net/10900/97903
http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-979033
http://dx.doi.org/10.15496/publikation-39286
Dokumentart: Wissenschaftlicher Artikel
Erscheinungsdatum: 2020-02-12
Originalveröffentlichung: University of Tübingen Working Papers in Business and Economics ; No. 130
Sprache: Englisch
Fakultät: 6 Wirtschafts- und Sozialwissenschaftliche Fakultät
Fachbereich: Wirtschaftswissenschaften
DDC-Klassifikation: 330 - Wirtschaft
Schlagworte: Rendite , Prognose , Maschinelles Lernen
Freie Schlagwörter:
stock risk premia
return forecasts
machine learning
theory-based return prediction
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Abstract:

We assess financial theory-based and machine learning-implied measurements of stock risk premia by comparing the quality of their return forecasts. In the low signal-to-noise environment of a one month horizon, we find that it is preferable to rely on a theory-based approach instead of engaging in the computerintensive hyper-parameter tuning of statistical models. The theory-based approach also delivers a solid performance at the one year horizon, at which only one machine learning methodology (random forest) performs substantially better. We also consider ways to combine the opposing modeling philosophies, and identify the use of random forests to account for the approximation residuals of the theory-based approach as a promising hybrid strategy. It combines the advantages of the two diverging paths in the finance world.

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