PT  - JOURNAL ARTICLE
AU  - Marcos López de Prado
TI  - Machine Learning for Econometricians: The Readme Manual
AID  - 10.3905/jfds.2022.1.101
DP  - 2022 Jul 31
TA  - The Journal of Financial Data Science
PG  - 10--30
VI  - 4
IP  - 3
4099  - https://pm-research.com/content/4/3/10.short
4100  - https://pm-research.com/content/4/3/10.full
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.