Three Machine Learning Solutions to the Bias-Variance Dilemma (Seminar Slides)

27 Pages Posted: 28 May 2020

See all articles by Marcos Lopez de Prado

Marcos Lopez de Prado

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

Date Written: April 29, 2020

Abstract

Classical statistics (e.g., Econometrics) relies on assumptions that are often unrealistic in finance. Two critical assumptions are that the researcher has perfect knowledge about the model’s specification, and that the researcher knows all the variables involved in a phenomenon (including all interaction effects). When those assumptions are incorrect, classical estimators are not guaranteed to be unbiased, or to be the most efficient among the unbiased, leading to poor performance.

In this presentation we explore why machine learning algorithms are generally more appropriate for financial datasets, how they outperform classical estimators, and how they solve the bias-variance dilemma.

Keywords: Bias, variance, MVUE, BLUE, econometrics, machine learning, ensemble, cross-validation, regularization

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

Suggested Citation

López de Prado, Marcos, Three Machine Learning Solutions to the Bias-Variance Dilemma (Seminar Slides) (April 29, 2020). Available at SSRN: https://ssrn.com/abstract=3588594 or http://dx.doi.org/10.2139/ssrn.3588594

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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