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Python for Finance Cookbook

You're reading from   Python for Finance Cookbook Over 50 recipes for applying modern Python libraries to financial data analysis

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781789618518
Length 432 pages
Edition 1st Edition
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Author (1):
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Eryk Lewinson Eryk Lewinson
Author Profile Icon Eryk Lewinson
Eryk Lewinson
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Table of Contents (12) Chapters Close

Preface 1. Financial Data and Preprocessing 2. Technical Analysis in Python FREE CHAPTER 3. Time Series Modeling 4. Multi-Factor Models 5. Modeling Volatility with GARCH Class Models 6. Monte Carlo Simulations in Finance 7. Asset Allocation in Python 8. Identifying Credit Default with Machine Learning 9. Advanced Machine Learning Models in Finance 10. Deep Learning in Finance 11. Other Books You May Enjoy

Implementing the four- and five-factor models in Python

In this recipe, we implement two extensions of the Fama-French three-factor model.

Carhart's Four-Factor model: The underlying assumption of this extension is that, within a short period of time, a winner stock will remain a winner, while a loser will remain a loser. An example of a criterion for classifying winners and losers could be the last 12-month cumulative total returns. After identifying the two groups, we long the winners and short the losers within a certain holding period.

The momentum factor (WML; Winners Minus Losers) measures the excess returns of the winner stocks over the loser stocks in the past 12 months (please refer to the See also section of this recipe for references on the calculations of the momentum factor).

The four-factor model can be expressed:

Fama-French's Five-Factor model: Fama...

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