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Python for Finance

You're reading from  Python for Finance

Product type Book
Published in Apr 2014
Publisher
ISBN-13 9781783284375
Pages 408 pages
Edition 1st Edition
Languages
Author (1):
Yuxing Yan Yuxing Yan
Profile icon Yuxing Yan

Table of Contents (20) Chapters

Python for Finance
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Introduction and Installation of Python Using Python as an Ordinary Calculator Using Python as a Financial Calculator 13 Lines of Python to Price a Call Option Introduction to Modules Introduction to NumPy and SciPy Visual Finance via Matplotlib Statistical Analysis of Time Series The Black-Scholes-Merton Option Model Python Loops and Implied Volatility Monte Carlo Simulation and Options Volatility Measures and GARCH Index

Geometric versus arithmetic mean


In the next section, we discuss long-term return forecasting. Since we apply the weighted arithmetic and geometric means, we need to familiarize ourselves with the geometric mean first. For n returns (,, ) their arithmetic and geometric means are defined as follows:

In this formula, Ri is the stock's ith return. For an arithmetic mean, we could use the mean() function. Most of the time, the arithmetic mean is used in our estimations because of its simplicity. Since geometric means consider the time of values, it is considered to be more accurate for returns' estimation based on historical data. One important feature is that the geometric mean is smaller than its corresponding arithmetic mean unless all input values, such as all returns, are all the same. Because of this feature, many argue that using arithmetic means to predict future returns would lead to an overestimation. In contrast, geometric means would lead to an underestimation. Since the geometric...

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