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; Abu Dhabi Investment Authority; True Positive Technologies

Date Written: April 29, 2020


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 and López de Prado, Marcos, Three Machine Learning Solutions to the Bias-Variance Dilemma (Seminar Slides) (April 29, 2020). Available at SSRN: or

Marcos López de Prado (Contact Author)

Cornell University - Operations Research & Industrial Engineering ( email )

237 Rhodes Hall
Ithaca, NY 14853
United States


Abu Dhabi Investment Authority ( email )

211 Corniche Road
Abu Dhabi, Abu Dhabi PO Box3600
United Arab Emirates


True Positive Technologies ( email )

United States


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