EAN: 9781108792899

MACHINE LEARNING FOR ASSET MANAGERS - MARCOS M ...

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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