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

YIELD of AAA-rated bond, Altman Z-score


From the previous sections, we have learnt that the spread between a bond's yield and a treasury bond's yield with the same maturity is the default risk premium. To retrieve the yields for AAA and AA bonds, we use the following codes. Moody's Seasoned Aaa Corporate Bond Yield can be downloaded at https://fred.stlouisfed.org/series/AAA. The dataset can be downloaded at http://canisius.edu/~yany/python/moodyAAAyield.p. Note that the .png of .p is fine for the .pickle format:

import pandas as pd
x=pd.read_pickle("c:/temp/moodyAAAyield.p")
print(x.head())
print(x.tail())

The output is shown here:

Note that the values of the second column, for the dataset called moodyAAAyield.p, are annualized. Thus, if we want to estimate a monthly yield (rate of return) in January 1919, the yield should be 0.4458333%, that is, 0.0535/12.

Altman's z-score is widely applied in finance for credit analysis to predict the possibility of a firm going to bankruptcy. This score is...