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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
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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 FREE CHAPTER 2. Data Preprocessing 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

Estimating the CAPM

In this recipe, we learn how to estimate the famous Capital Asset Pricing Model (CAPM) and obtain the beta coefficient. This model represents the relationship between the expected return on a risky asset and the market risk (also known as systematic or undiversifiable risk). CAPM can be considered a one-factor model, on top of which more complex factor models were built.

CAPM is represented by the following equation:

Here, E(ri) denotes the expected return on asset i, rf is the risk-free rate (such as a government bond), E(rm) is the expected return on the market, and is the beta coefficient.

Beta can be interpreted as the level of the asset return’s sensitivity, as compared to the market in general. Below we mention the possible interpretations of the coefficient:

  • <= -1: The asset moves in the opposite direction to the benchmark and in a greater amount than the negative of the benchmark.
  • -1 < < 0: The asset...
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