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

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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: Aufsatz
Date: 2020-02-12
Source: University of Tübingen Working Papers in Business and Economics ; No. 130
Language: English
Faculty: 6 Wirtschafts- und Sozialwissenschaftliche Fakultät
Department: Wirtschaftswissenschaften
DDC Classifikation: 330 - Economics
Keywords: Rendite , Prognose , Maschinelles Lernen
Other Keywords:
stock risk premia
return forecasts
machine learning
theory-based return prediction
License: Publishing license excluding print on demand
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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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