Machine Learning for Asset Managers
(Part of the Elements in Quantitative Finance Series)
No Customer Reviews
Select Format
Select Condition 
Book Overview
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:Paperback
Language:English
ISBN:1108792898
ISBN13:9781108792899
Release Date:April 2020
Publisher:Cambridge University Press
Length:152 Pages
Weight:0.60 lbs.
Dimensions:0.3" x 6.0" x 9.0"
Customer Reviews
0 rating
Copyright © 2025 Thriftbooks.com
Terms of Use | Privacy Policy | Do Not Sell/Share My Personal Information | Cookie Policy | Cookie Preferences | Accessibility Statement
ThriftBooks ® and the ThriftBooks ® logo are registered trademarks of Thrift Books Global, LLC
ThriftBooks ® and the ThriftBooks ® logo are registered trademarks of Thrift Books Global, LLC
