Skip to Content
Hands-On Machine Learning for Algorithmic Trading
book

Hands-On Machine Learning for Algorithmic Trading

by Stefan Jansen
December 2018
Beginner to intermediate
684 pages
21h 9m
English
Packt Publishing
Content preview from Hands-On Machine Learning for Algorithmic Trading

Compute factor betas

We will introduce the Fama—French data to estimate the exposure of assets to common risk factors using linear regression in Chapter 8, Time Series Models. The five Fama—French factors, namely market risk, size, value, operating profitability, and investment have been shown empirically to explain asset returns and are commonly used to assess the risk/return profile of portfolios. Hence, it is natural to include past factor exposures as financial features in models that aim to predict future returns.

We can access the historical factor returns using the pandas-datareader and estimate historical exposures using the PandasRollingOLS rolling linear regression functionality in the pyfinance library as follows:

factors = [
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Machine Learning for Algorithmic Trading - Second Edition

Machine Learning for Algorithmic Trading - Second Edition

Stefan Jansen

Publisher Resources

ISBN: 9781789346411Supplemental Content