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Python for Finance Cookbook – Second Edition

You're reading from   Python for Finance Cookbook – Second Edition Over 80 powerful recipes for effective financial data analysis

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781803243191
Length 740 pages
Edition 2nd Edition
Languages
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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 (18) Chapters Close

Preface 1. Acquiring Financial Data 2. Data Preprocessing FREE CHAPTER 3. Visualizing Financial Time Series 4. Exploring Financial Time Series Data 5. Technical Analysis and Building Interactive Dashboards 6. Time Series Analysis and Forecasting 7. Machine Learning-Based Approaches to Time Series Forecasting 8. Multi-Factor Models 9. Modeling Volatility with GARCH Class Models 10. Monte Carlo Simulations in Finance 11. Asset Allocation 12. Backtesting Trading Strategies 13. Applied Machine Learning: Identifying Credit Default 14. Advanced Concepts for Machine Learning Projects 15. Deep Learning in Finance 16. Other Books You May Enjoy
17. Index

Multi-Factor Models

This chapter is devoted to estimating various factor models. Factors are variables/attributes that in the past were correlated with (then future) stock returns and are expected to contain the same predictive signals in the future.

These risk factors can be considered a tool for understanding the cross-section of (expected) returns. That is why various factor models are used to explain the excess returns (over the risk-free rate) of a certain portfolio or asset using one or more factors. We can think of the factors as the sources of risk that are the drivers of those excess returns. Each factor carries a risk premium and the overall portfolio/asset return is the weighted average of those premiums.

Factor models play a crucial role in portfolio management, mainly because:

  • They can be used to identify interesting assets that can be added to the investment portfolio, which—in turn—should lead to better-performing portfolios.
  • Estimating...
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