Python for Finance: Are stock returns normally distributed?

Today we investigate whether stock returns are normally distributed! First, I show the difference between simple returns and log returns, highlighting the main reason log returns are preferred for financial analysis.

Then we explore whether CBA (ASX listed stock, Commonwealth Bank of Australia) log returns are normally distributed. We do this by considering several visual and statistical techniques; Quantile-Quantile Plots, Box Plots, Histograms, and numerically by Hypothesis Testing with Kolmogorov Smirnov and Shapiro Wilk tests.

00:00 Intro
00:27 Why use log returns?
02:18 Simple returns
07:28 Log returns
10:44 Are log returns normally distributed?
16:08 Quantile-Quantile Plots
18:00 Box Plots
19:20 Kolmogorov Smirnov test
22:10 Shapiro Wilk tests

As a high-level programming language, Python is a great tool for financial data analysis, with quick implementation and well documented API data sources, statistical modules and other frameworks related to the financial industry. We will be using Jupyter Lab as an interactive web browser editor for this series due to ease of use and presenting code in a live notebook is ideal for this tutorial series.

This is the third video of many on the topic of Python for Finance. The series will include general techniques used for financial analysis and act as an introduction for more in-depth tutorials that we may explore later (such as time series modelling, building financial dashboards, machine learning ect.).


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