Machine Learning for Asset Managers
$69.67
- Paperback
152 pages
- Release Date
30 April 2020
Summary
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 necessari…
Book Details
| ISBN-13: | 9781108792899 |
|---|---|
| ISBN-10: | 1108792898 |
| Author: | Marcos M. López de Prado |
| Publisher: | Cambridge University Press |
| Imprint: | Cambridge University Press |
| Format: | Paperback |
| Number of Pages: | 152 |
| Release Date: | 30 April 2020 |
| Weight: | 250g |
| Dimensions: | 230mm x 152mm x 12mm |
| Series: | Elements in Quantitative Finance |
What They're Saying
Critics Review
‘The book’s excellent introduction explains why machine learning techniques will benefit asset managers substantially and why traditional or classical linear techniques have limitations and are often inadequate in asset management. It makes a strong case that ML is not a black box but a set of data tools that enhance theory and improve data clarity. López de Prado focuses on seven complex problems or topics where applying new techniques developed by ML specialists will add value.’ Mark S. Rzepczynski, Enterprising Investor
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