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

Joachim Grammig, Constantin Hanenberg, Christian Schlag and Jantje Sönksen

No 130, University of Tübingen Working Papers in Business and Economics from University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics

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.

Keywords: stock risk premia; return forecasts; machine learning; theorybased return prediction (search for similar items in EconPapers)
JEL-codes: C53 C58 G12 G17 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:tuewef:130

DOI: 10.15496/publikation-39286

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