Machine Learning for Asset Managers
Machine Learning for Asset Managers Successful investment strategies are specific implementations of general theories. Hence, an asset manager should concentrate her efforts on developing a theory…
Specifikacia Machine Learning for Asset Managers
Machine Learning for Asset Managers
Successful investment strategies are specific implementations of general theories. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. An investment strategy that lacks a theoretical justification is likely to be false.
ML is not a black box, and it does not necessarily overfit. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. 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
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