Machine Learning for Asset Managers (Elements in Quantitative Finance)
Machine Learning for Asset Managers (Elements in Quantitative Finance)

Read Online Machine Learning for Asset Managers (Elements in Quantitative Finance) MOBI

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.

  • Released: November 16, 2020
  • Author: Marcos López de Prado
  • Rating: 4,6
  • Publisher: Ebook DIgital Agency
  • Total Page: 562 pages
  • File Size: 3670 KB

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