Machine Learning for Asset Managers

Erscheinungsjahr 2020
ISBN: 978-1-108-79289-9
Verlag: Cambridge University Pr.

Buch, Englisch, 152 Seiten, Format (B × H): 153 mm x 228 mm, Gewicht: 229 g

Reihe: Elements in Quantitative Finance

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.

Autoren/Hrsg.

López de Prado, Marcos M.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines

Inhaltsverzeichnis

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.