In this chapter, we will be solving a problem that absolutely interests everyone—predicting stock price. Gaining wealth by smart investment, who doesn't! In fact, stock market movements and stock price prediction has been actively researched by a large number of financial and trading, and even technology, corporations. A variety of methods have been developed to predict stock price using machine learning techniques. We herein will be focusing on learning several popular regression algorithms including linear regression, regression tree and regression forest, as well as support vector regression, and utilizing them to tackle this billion (or trillion) dollar problem.
We will cover the following topics in this chapter:
- Introduction to the stock market and stock price
- What is regression?
- Feature engineering
- Acquiring stock data and generating predictive features
- What is linear regression?
- The mechanics of linear regression
- The implementations of linear regression
- What is decision tree regression?
- The mechanics of regression tree
- The implementations of regression tree
- From regression tree to regression forest
- What is support vector regression?
- The mechanics of support vector regression
- The implementations of support vector regression
- Regression performance evaluation
- Predicting stock price with regression algorithms