Machine Learning for Asset Managers (Elements in Quantitative Finance) - Tapa dura

López De Prado, Marcos M.

 
9781009702423: Machine Learning for Asset Managers (Elements in Quantitative Finance)
Tapa dura
ISBN 10: 1009702424 ISBN 13: 9781009702423
Editorial: Cambridge University Press, 2026

Sinopsis

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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Editorial
Cambridge University Press
Año de publicación
2026
Idioma
Inglés
ISBN 10 
1009702424
ISBN 13 
9781009702423
Encuadernación
Tapa dura
Número de páginas
152
Contacto del fabricante
Cambridge University Press
https://www.cambridge.org/

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