Machine Learning for Asset Managers (Elements in Quantitative Finance)

L�pez de Prado, Marcos M. Marcos M. L�pez de Prado,

ISBN 10: 1009702424 / ISBN 13: 9781009702423

Published by Cambridge University Press, 2026

About this Item

Seller Inventory # 6666-GRD-9781009702423

Bibliographic Details

• Title: Machine Learning for Asset Managers (Elements in Quantitative Finance)
• Publisher: Cambridge University Press
• Publication Date: 2026
• Language: English
• ISBN 10: 1009702424
• ISBN 13: 9781009702423
• Binding: hardcover
• Condition: New

About this title

Synopsis

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.