RT Journal Article
SR Electronic
T1 Machine Learning for Econometricians: The Readme Manual
JF The Journal of Financial Data Science
FD Institutional Investor Journals
SP jfds.2022.1.101
DO 10.3905/jfds.2022.1.101
A1 Marcos López de Prado
YR 2022
UL https://pm-research.com/content/early/2022/07/09/jfds.2022.1.101.abstract
AB One of the most exciting recent developments in financial research is the availability of new administrative, private sector, and micro-level datasets that did not exist a few years ago. The unstructured nature of many of these observations, along with the complexity of the phenomena they measure, means that many of these datasets are beyond the grasp of econometric analysis. Machine learning (ML) techniques offer the numerical power and functional flexibility needed to identify complex patterns in a high-dimensional space. ML is often perceived as a black box, however, in contrast to the transparency of econometric approaches. In this article, the author demonstrates that each analytical step of the econometric process has a homologous step in ML analyses. By clearly stating this correspondence, the author’s goal is to facilitate and reconcile the adoption of ML techniques among econometricians.