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Series:   Elements in Quantitative Finance

Machine Learning for Asset Managers

Published online by Cambridge University Press:  04 April 2020

Marcos M. López de Prado
Marcos M. López de Prado
Affiliation:
Cornell University, New York

Summary

Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to “learn” complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.
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Keywords

machine learningunsupervised learningsupervised learningclusteringclassificationlabelingportfolio construction
Type
Element
Information
Series: Elements in Quantitative Finance
Online ISBN: 9781108883658
Publisher: Cambridge University Press
Print publication: 30 April 2020

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