If you’ve ever heard someone say a stock’s price is “three standard deviations away from the mean,” they’re talking about Z-Scores. In Chapter 11 of Data Analytics for Finance Using Python, we explore how to use this tool to find the “weird” data points that might actually be opportunities.
We just talked about the F-Test for comparing risk (variance), but what if you want to know if two stocks actually earn different amounts on average? In Chapter 10 of Data Analytics for Finance Using Python, we look at the T-Test.
If you’re choosing between two stocks, you don’t just want to know which one has a higher return. you want to know which one is more likely to give you a heart attack. In Chapter 9 of Data Analytics for Finance Using Python, we look at the F-Test as a way to compare risk.
In the world of finance, nothing happens in a vacuum. A stock’s closing price isn’t just a random number; it’s influenced by the opening price, the daily high, the daily low, and a dozen other factors.
Before you start building complex machine learning models, you need to understand the data you’re actually working with. In Chapter 7 of Data Analytics for Finance Using Python, we go back to the basics: Descriptive Statistics.