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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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Toc

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

Estimating Pastor and Stambaugh (2003) liquidity measure

Based on the methodology and empirical evidence in Campbell, Grossman, and Wang (1993), Pastor and Stambaugh (2003) designed the following model to measure individual stock's liquidity and the market liquidity:

Estimating Pastor and Stambaugh (2003) liquidity measure

Here, yt is the excess stock return, Rt-Rf , t, on day t, Rt is the return for the stock, Rf,t is the risk-free rate, x1,t is the market return, and x2,t is the signed dollar trading volume:

Estimating Pastor and Stambaugh (2003) liquidity measure

pt is the stock price, and volume, t is the trading volume. The regression is run based on daily data for each month. In other words, for each month, we get one β2 that is defined as the liquidity measure for individual stock. The following code estimates the liquidity for IBM. First, we download the IBM and S&P500 daily price data, estimate their daily returns, and merge them as follows:

import numpy as np 
from matplotlib.finance import quotes_historical_yahoo_ochl as getData
import numpy as np 
import pandas as pd 
import...
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