Book Image

Python for Finance - Second Edition

By : Yuxing Yan
5 (1)
Book Image

Python for Finance - Second Edition

5 (1)
By: Yuxing Yan

Overview of this book

This book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM’s market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option.
Table of Contents (23 chapters)
Python for Finance Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

References


Please refer to the following articles:

  • Carhart, Mark M., 1997, On Persistence in Mutual Fund Performance, Journal of Finance 52, 57-82.

  • Fama, Eugene and Kenneth R. French, 1993, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics 33, 3056.

  • Fama, Eugene and Kenneth R. French, 1992, The cross-section of expected stock returns, Journal of Finance 47, 427-465.

  • String manipulation: http://www.pythonforbeginners.com/basics/string-manipulation-in-python

Appendix A – data case #3 - beta estimation

Objective: hands-on experience to estimate the market risk for a given set of companies:

  1. What are alpha and beta for those companies?

  2. Comment on your results.

  3. Based on your monthly returns, what are the means of annual returns for S&P500 and risk-free rate?

  4. If the expected market return is 12.5% per year and the expected risk-free rate is 0.25% per year, what are the costs of equity for those companies?

  5. What is the portfolio beta?

    Computational tool: Python

    Period...