  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.
