Machine Learning for Asset Managers

Language: English

Published by Cambridge University Press, GB, 2026

1009702424 / 9781009702423

Series: Book 1 of 7 - Elements in Quantitative Finance

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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.…

Seller Inventory # LU-9781009702423

Title
Machine Learning for Asset Managers
Author
Marcos M. López de Prado
Publisher
Cambridge University Press, GB
Publication year
2026
Condition
New
Binding
Hardback
Language
English
ISBN 10
1009702424
ISBN 13
9781009702423
Item weight
365 grams
Series
Book 1 of 7: Elements in Quantitative Finance

Rarewaves.com UK

London, United Kingdom

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