Beyond Econometrics: A Roadmap Towards Financial Machine Learning

31 Pages Posted: 22 Apr 2019 Last revised: 23 Sep 2019

See all articles by Marcos Lopez de Prado

Marcos Lopez de Prado

Cornell University - Operations Research & Industrial Engineering; True Positive Technologies

Date Written: September 19, 2019

Abstract

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. However, ML is often perceived as a black box, in contrast with the transparency of econometric approaches. This article demonstrates that each analytical step of the econometric process has a homologous step in ML analyses. By clearly stating this correspondence, our goal is to facilitate and reconcile the adoption of ML techniques among econometricians.

Keywords: machine learning, artificial intelligence, econometrics, financial economics

JEL Classification: G0, G1, G2, G15, G24, E44

Suggested Citation

López de Prado, Marcos, Beyond Econometrics: A Roadmap Towards Financial Machine Learning (September 19, 2019). Available at SSRN: https://ssrn.com/abstract=3365282 or http://dx.doi.org/10.2139/ssrn.3365282

Marcos López de Prado (Contact Author)

Cornell University - Operations Research & Industrial Engineering ( email )

237 Rhodes Hall
Ithaca, NY 14853
United States

HOME PAGE: http://www.orie.cornell.edu

True Positive Technologies ( email )

NY
United States

HOME PAGE: http://www.truepositive.com

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