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Machine Learning for Asset Managers

By (author) Marcos M. Lopez 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.

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Helgoland: The Strange and Beautiful Story of Quantum Physics

Carlo Rovelli
Paperback / softback
208 pages
was: R 295.95
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The Coming Wave: AI, Power and Our Future

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Paperback / softback
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The Order of Time

Carlo Rovelli
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224 pages
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Originally published in Italian: L'ordine del tempo (Milan: Adelphi Edizioni, 2017).

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