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Python for Finance

You're reading from   Python for Finance Apply powerful finance models and quantitative analysis with Python

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787125698
Length 586 pages
Edition 2nd Edition
Languages
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Author (1):
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Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Table of Contents (17) Chapters Close

Preface 1. Python Basics FREE CHAPTER 2. Introduction to Python Modules 3. Time Value of Money 4. Sources of Data 5. Bond and Stock Valuation 6. Capital Asset Pricing Model 7. Multifactor Models and Performance Measures 8. Time-Series Analysis 9. Portfolio Theory 10. Options and Futures 11. Value at Risk 12. Monte Carlo Simulation 13. Credit Risk Analysis 14. Exotic Options 15. Volatility, Implied Volatility, ARCH, and GARCH Index

Introduction to CAPM

According to the famous CAPM, the expected returns of a stock are linearly correlated with expected market returns. Here, we use the international business machine with a ticker of IBM as an example and this linear one-factor asset pricing model could be applied to any other stocks or portfolios. The formula is given here:

Introduction to CAPM

Here, E() is the expectation, E(RIBM) is the expected return for IBM, Rf is the risk-free rate, and E(Rmkt) is the expected market return. For instance, the S&P500 index could be served as a market index. The slope of the preceding equation or Introduction to CAPM is a measure of IBM's market risk. To make our notation simpler, the expectation could be dropped:

Introduction to CAPM

Actually, we could consider the relationship between the excess stock returns and the excess market returns. The following formula is essentially the same as the preceding formula, but it has a better and clearer interpretation:

Introduction to CAPM

Recall that in Chapter 3, Time Value of Money, we learnt that the difference...

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