Description: 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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Location: Hillsdale, NSW
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Restocking Fee: No
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 60 Days
Refund will be given as: Money back or replacement (buyer's choice)
EAN: 9781108792899
UPC: 9781108792899
ISBN: 9781108792899
MPN: N/A
Book Title: Machine Learning for Asset Managers (Elements in Q
Number of Pages: 152 Pages
Language: English
Publication Name: Machine Learning for Asset Managers
Publisher: Cambridge University Press
Subject: Finance / General
Publication Year: 2020
Item Height: 0.5 in
Type: Textbook
Item Weight: 8.8 Oz
Author: Marcos Lopez De Prado
Item Length: 9.1 in
Subject Area: Business & Economics
Item Width: 6 in
Series: Elements in Quantitative Finance Ser.
Format: Trade Paperback
