Book Image

Python for Finance

By : Yuxing Yan
Book Image

Python for Finance

By: Yuxing Yan

Overview of this book

A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.
Table of Contents (14 chapters)
13
Index

Graphical representation of the portfolio diversification effect

In finance, we could remove firm-specific risk by combining different stocks in our portfolio. First, let us look at a hypothetical case by assuming that we have 5 years' annual returns of two stocks as follows:

Year

Stock A

Stock B

2009

0.102

0.1062

2010

-0.02

0.23

2011

0.213

0.045

2012

0.12

0.234

2013

0.13

0.113

We form an equal-weighted portfolio using those two stocks. Using the mean() and std() functions contained in NumPy, we can estimate their means, standard deviations, and correlation coefficients as follows:

>>>import numpy as np
>>>A=[0.102,-0.02, 0.213,0.12,0.13]
>>>B=[0.1062,0.23, 0.045,0.234,0.113]
>>>port_EW=(np.array(ret_A)+np.array(ret_B))/2.
>>>round(np.mean(A),3),round(np.mean(B),3),round(np.mean(port_EW),3)
(0.109, 0.146, 0.127)
>>>round(np.std(A),3),round(np.std(B),3),round(np.std(port_EW),3)
(0.075, 0.074, 0.027)

In the preceding...