Diverging Roads: Theory-Based vs. Machine Learning-Implied Stock Risk Premia.
We compare the performance of theory-based and machine learning (ML) methods for quantifying equity risk premia and assess hybrid strategies that combine the two very different philosophies. The theory-based approach offers advantages at a one-month investment horizon, in particular, if daily freque...
| Published in: | Journal of Financial Econometrics Vol. 23; no. 2; pp. 1 - 56 |
|---|---|
| Main Authors: | , , , |
| Format: | Article |
| Published: |
Oxford University Press / USA
2025
|
| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=184193010&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 184193010 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 14798409 T2Y jtl: Journal of Financial Econometrics issn: 14798409 maglogo: N pubinfo: dt: 2025 vid: 23 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 184193010 10.1093/jjfinec/nbaf005 ppf: 1 ppct: 55 formats: tig: atl: Diverging Roads: Theory-Based vs. Machine Learning-Implied Stock Risk Premia. aug: au: Grammig, Joachim Hanenberg, Constantin Schlag, Christian Sönksen, Jantje affil: Department of Statistics, Econometrics and Empirical Economics, University of Tübingen, Mohlstrasse 36, Tübingen, 72074, Germany Centre for Financial Research (CFR), Albertus-Magnus-Platz, Cologne, 50923, Germany House of Finance, Goethe University Frankfurt, Theodor-W.-Adorno-Platz 1, Frankfurt/Main, 60629, Germany Leibniz Institute for Financial Research SAFE, Theodor-W.-Adorno-Platz 3, Frankfurt/Main, 60323, Germany Institute for Econometrics and Data Science, Leibniz University Hannover, Königsworther Platz 1, Hannover, 30167, Germany su: Risk premiums Machine learning Random forest algorithms Stock options Approximation error sug: subj: Investment Banking and Securities Dealing Risk premiums Machine learning Random forest algorithms Stock options Approximation error keyword: C53 C58 G12 G17 machine learning option prices stock risk premia C53 C58 G12 G17 machine learning option prices stock risk premia ab: We compare the performance of theory-based and machine learning (ML) methods for quantifying equity risk premia and assess hybrid strategies that combine the two very different philosophies. The theory-based approach offers advantages at a one-month investment horizon, in particular, if daily frequency risk premium estimates (RPE) are needed. At the one-year horizon, ML has an edge, especially using theory-based RPE as additional feature variables. For a hybrid strategy called Theory with ML Assistance , we employ ML to account for the approximation errors of the theory-based approach. Employing random forests or an ensemble of ML models for theory support yields promising results. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|