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

Buch 1�von 7: Elements in Quantitative Finance

L�pez De Prado, Marcos M.

9781009702423: Machine Learning for Asset Managers (Elements in Quantitative Finance)

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ISBN 10: 1009702424 ISBN 13: 9781009702423

Verlag: Cambridge University Press, 2026

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This element introduces machine learning (ML) tools that can help asset managers discover economic and financial theories.

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Verlag: Cambridge University Press
Erscheinungsdatum: 2026
Sprache: Englisch
ISBN 10: 1009702424
ISBN 13: 9781009702423
Einband: Gebundene Ausgabe
Anzahl der Seiten: 152
Kontakt zum Hersteller: Cambridge University Press
https://www.cambridge.org/

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Machine Learning for Asset Managers (Elements in Quantitative Finance)

L� pez de Prado, Marcos M.

Verlag: Cambridge University Press, 2026

ISBN 10: 1009702424 ISBN 13: 9781009702423

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Machine Learning For Asset Managers

Lopez De Prado, Marcos M. (cornell University, New York)

Verlag: Cambridge University Press, 2026

ISBN 10: 1009702424 ISBN 13: 9781009702423

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Machine Learning For Asset Managers

Lopez De Prado, Marcos M. (Cornell University, New York)

Verlag: Cambridge University Press, 2026

ISBN 10: 1009702424 ISBN 13: 9781009702423

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Machine Learning For Asset Managers

Lopez De Prado, Marcos M. (cornell University, New York)

Verlag: Cambridge University Press, 2026

ISBN 10: 1009702424 ISBN 13: 9781009702423

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Machine Learning for Asset Managers (Hardcover)

Marcos M. Lopez de Prado

Verlag: Cambridge University Press, Cambridge, 2026

ISBN 10: 1009702424 ISBN 13: 9781009702423

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Hardcover. Zustand: new. Hardcover. 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. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verk�ufers 9781009702423

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Machine Learning for Asset Managers

Marcos M. L�pez de Prado

Verlag: Cambridge University Press, GB, 2026

ISBN 10: 1009702424 ISBN 13: 9781009702423

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Hardback. Zustand: New. 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. Bestandsnummer des Verk�ufers LU-9781009702423

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