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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

Performance measures

To compare the performance of mutual functions or individual stocks, we need a performance measure. In finance, we know that investors should seek a trade-off between risk and returns. It might not be a good idea to say that portfolio A is better than portfolio B since the former offered us a 30% return last year while the latter offered just 8%. The obvious reason is that we should not ignore risk factors. Because of this, we often hear the phrase "risk-adjusted return". In this section, the Sharpe ratio, Treynor ratio, Sortino ratio, and Jensen's alpha will be discussed. The Sharpe ratio is a widely used performance measure and it is defined as follows:

Performance measures

Here, Performance measures is the mean return for a portfolio or a stock, Performance measures is the mean return for a risk-free security, σ is the variance of the excess portfolio (stock) return, and VaR is the variance of the excess portfolio (stock) return. The following code is used to estimate the Sharpe ratio with a hypothetical...

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