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Python for Finance

Python for Finance

By : Yuxing Yan
3.9 (22)
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Python for Finance

Python for Finance

3.9 (22)
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)
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13
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 (Geometric versus arithmetic mean,Geometric versus arithmetic mean, Geometric versus arithmetic mean) their arithmetic and geometric means are defined as follows:

Geometric versus arithmetic mean
Geometric versus arithmetic mean

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...

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