Confronting Machine Learning with Financial Research

34 Pages Posted: 22 Mar 2021 Last revised: 26 Mar 2021

See all articles by Kristof Lommers

Kristof Lommers

University of Oxford, Said Business School

Ouns El Harzli

University of Oxford; Mines-Paristech

Jack Kim

Stanford University - Department of Management Science & Engineering

Date Written: February 18, 2021

Abstract

This study aims to examine the challenges and applications of machine learning for financial research. Machine learning algorithms have been developed for certain data environments which substantially differ from the one we encounter in finance. Not only do difficulties arise due to some of the idiosyncrasies of financial markets, there is a fundamental tension between the underlying paradigm of machine learning and the research philosophy in financial economics. Given the peculiar features of financial markets and the empirical framework within social science, various adjustments have to be made to the conventional machine learning methodology. We discuss some of the main challenges of machine learning in finance and examine how these could be accounted for. Despite some of the challenges, we argue that machine learning could be unified with financial research to become a robust complement to the econometrician’s toolbox. Moreover, we discuss the various applications of machine learning in the research process such as estimation, empirical discovery, testing, causal inference and prediction.

Keywords: Financial Machine Learning, Empirical Finance, Financial Econometrics

JEL Classification: G00, C10, C40

Suggested Citation

Lommers, Kristof and El Harzli, Ouns and Kim, Jack, Confronting Machine Learning with Financial Research (February 18, 2021). Available at SSRN: https://ssrn.com/abstract=3788349 or http://dx.doi.org/10.2139/ssrn.3788349

Kristof Lommers (Contact Author)

University of Oxford, Said Business School ( email )

Oxford, OX1 5NY
United Kingdom

Ouns El Harzli

University of Oxford ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

Mines-Paristech ( email )

60 Boulevard Saint-Michel
Paris, 75272
France

Jack Kim

Stanford University - Department of Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
89
Abstract Views
297
rank
338,378
PlumX Metrics