Confronting Machine Learning With Financial Research

TL;DR

This paper explores the challenges and opportunities of applying machine learning to financial research, highlighting necessary adjustments and potential for integration with traditional econometric methods.

Contribution

It identifies key challenges of machine learning in finance and proposes ways to adapt methodologies for effective application in financial research.

Findings

Machine learning faces unique challenges in financial data environments.

Adjustments are needed to align machine learning with financial research paradigms.

Machine learning can complement traditional econometrics in finance.

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.