Machine Learning for Asset Managers

Marcos M. López de Prado (Autor)

Buch | Hardcover

2026
Cambridge University Press (Verlag)
978-1-009-70242-3 (ISBN)

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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.

1. Introduction; 2. Denoising and detoning; 3. Distance metrics; 4. Optimal clustering; 5. Financial labels; 6. Feature importance analysis; 7. Portfolio construction; 8. Testing set overfitting.

Erscheint lt. Verlag	31.1.2026
Reihe/Serie	Elements in Quantitative Finance
Zusatzinfo	Worked examples or Exercises; 4 Tables, black and white; 30 Line drawings, black and white
Verlagsort	Cambridge
Sprache	englisch
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung
ISBN-10	1-009-70242-4 / 1009702424
ISBN-13	978-1-009-70242-3 / 9781009702423
Zustand	Neuware