Machine Learning for Asset Managers Book Reviews
Machine Learning for Asset Managers by Marcos M. López de Prado Book Summary
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.
Book Name | Machine Learning for Asset Managers |
Genre | Finance |
Author | Marcos M. López de Prado |
Published | 30 April 2020, Thursday |
Language | English |
E-Book Size | 10.99 MB |
Machine Learning for Asset Managers (Marcos M. López de Prado) Book Reviews 2024
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Summary of Machine Learning for Asset Managers by Marcos M. López de Prado
The Machine Learning for Asset Managers book written by Marcos M. López de Prado was published on 30 April 2020, Thursday in the Finance category. A total of 100 readers of the book gave the book 0 points out of 5.
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