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Python for Finance

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

Product type Paperback

Published in Jun 2017

Publisher

ISBN-13 9781787125698

Length 586 pages

Edition 2nd Edition

Languages

Python

Tools

Matplotlib

Concepts

Data Analysis

Table of Contents (23) Chapters

Python for Finance Second Edition

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Python Basics FREE CHAPTER

2. Introduction to Python Modules

3. Time Value of Money

4. Sources of Data

Diving into deeper concepts

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

References

Altman, Edward I, 1968, Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy, Journal of Finance,189–209, http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1968.tb00843.x/abstract
Altman, E.I., Kishore, V., 1997. Default and returns in the high yield debt market, 1991-1996,NYU Salomon Center Special Report
Altman, Edward I.,2000, PREDICTING FINANCIAL DISTRESS OF COMPANIES, http://pages.stern.nyu.edu/~ealtman/Zscores.pdf
Eidleman, Gregory J.,1995,Z-Scores – A Guide to Failure Prediction, CPA Journal Online, https://www.easycalculation.com/statistics/altman-z-score.php
Fitch, https://www.fitchratings.com/site/home.
KMV model, https://github.com/ghlingjun/kmv-model
Moody's website, http://www.moodys.com/
Moody's, 2007, Introducing Moody's Credit Transition Model, http://www.moodysanalytics.com/~/media/Brochures/Credit-Research-Risk-Measurement/Quantative-Insight/Credit-Transition-Model/Introductory-Article-Credit-Transition-Model.pdf
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