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

Summary


In this chapter, many concepts and issues associated with time-series are discussed in detail. Topics include how to design a true date variable, how to merge datasets with different frequencies, how to download historical prices from Yahoo! Finance; also, different ways to estimate returns, estimate the Roll (1984) spread, Amihud's (2002) illiquidity, Pastor and Stambaugh's (2003) liquidity, and how to retrieve high-frequency data from Prof. Hasbrouck's TORQ database (Trade, Oder, Report and Quotation). In addition, two datasets from CRSP are shown. Since this book is focusing on open and publicly available finance, economics, and accounting data, we could mention a few financial databases superficially.

In the next chapter, we discuss many concepts and theories related to portfolio theory such as how to measure portfolio risk, how to estimate the risk of 2-stock and n-stock portfolio, the trade-off between risk and return by using various measures of Sharpe ratio, Treynor ratio...