Machine Learning for Asset Managers

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.

List of contents

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.

About the author

Cornell University, New York

Summary

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.

Product details

• Authors: Marcos Lopez de Prado, Marcos (Cornell University Lopez De Prado, Marcos M. (Cornell University Lopez De Prado, Marcos López de Prado, Marcos M. López de Prado
• Publisher: Cambridge University Press Academic
• Languages: English
• Product format: Paperback / Softback
• Released: 31.03.2020
• EAN: 9781108792899
• ISBN: 978-1-108-79289-9
• Dimensions: 153 mm x 230 mm x 8 mm
• Series: Print on demand; Elements in Quantitative Finance
• Subjects: Social sciences, law, business > Business > Business administration; machine learning, Finance, BUSINESS & ECONOMICS / Finance / General, Finance and the finance industry