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Machine Learning for Asset Managers

Marcos M. López de Prado
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      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.
      Format: CONTRIBUTORS: Marcos M. López de Prado EAN: 9781108792899 COUNTRY: United Kingdom PAGES: WEIGHT: 250 g HEIGHT: 230 cm
      PUBLISHED BY: Cambridge University Press DATE PUBLISHED: 2020-04-30 CITY: GENRE: BUSINESS & ECONOMICS / Finance / General WIDTH: 152 cm SPINE:

      Book Themes:

      Finance and the finance industry, Machine learning

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      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.
      Format: CONTRIBUTORS: Marcos M. López de Prado EAN: 9781108792899 COUNTRY: United Kingdom PAGES: WEIGHT: 250 g HEIGHT: 230 cm
      PUBLISHED BY: Cambridge University Press DATE PUBLISHED: 2020-04-30 CITY: GENRE: BUSINESS & ECONOMICS / Finance / General WIDTH: 152 cm SPINE:

      Book Themes:

      Finance and the finance industry, Machine learning

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