TY  - BOOK
AU  - López de Prado,Marcos Mailoc
TI  - Machine learning for asset managers
T2  - Cambridge elements. Elements in quantitative finance, 2631-8571
SN  - 9781108883658 (ebook)
AV  - HG1615.25  .L66 2020
U1  - 332.10681 23
PY  - 2020///
CY  - Cambridge
PB  - Cambridge University Press
KW  -  Asset-liability management
KW  - Data processing
KW  - Machine learning
N1  - Title from publisher's bibliographic system (viewed on 08 Apr 2020)
N2  - 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
UR  - https://doi.org/10.1017/9781108883658
ER  -