Machine learning technology is used in every business scene. Among them, stock price forecasting is one of the hottest areas in the machine learning usage scene. Services that support stock price forecasts with AI are also appearing. Therefore, in this article, we will introduce specific methods and examples of stock price forecasting by machine learning.
Is machine learning used to predict stock prices?
First, let’s take a look at the definitions of “stock price” and “machine learning”.
What is a stock price in the first place?
The stock price is the price at which the stock was actually executed in the stock market. Stocks of listed companies can be freely bought and sold on the stock exchange, and the stock price is determined by the person who wants to buy and the person who wants to sell the stock.
What is machine learning?
Machine learning is a data analysis technique in which a computer learns a large amount of data to extract data rules and patterns. In recent years, it has become more important to make “prediction / judgment” based on the results learned.
There are different types of machine learning
There are five types of machine learning for learning the rules and patterns behind the data: “supervised learning,” “unsupervised learning,” “enhanced learning,” “deep reinforcement learning,” and “semi-supervised learning.” I have.
What algorithm is used for stock price forecasting?
Algorithms used for stock price forecasts include neural networks and random forests.
A neural network is a model that combines neurons, which are mathematical models inspired by neurons in the human cranial nerve system, into a layered network. By enlarging and complicating this neural network, high performance is demonstrated in various tasks.
Random forest, on the other hand, is an algorithm that predicts each class in multiple different classification trees and decides which class to classify by majority vote. It is easy to handle because there are few parameters that must be determined in advance.
An important factor in stock price forecasting
Stock prices are directly determined by investors’ buy and sell orders, but their investment behavior is influenced by a variety of factors. Here, we will introduce four important factors in stock price forecasting.
Profit
The stock price of a company that is making a high profit rises because the profit per share is also high. The stock price reflects the average future profit of the company expected by investors. Therefore, it does not necessarily reflect the business condition at that time. You need to be careful about that.
interest rate
When interest rates are high, stock prices fall because investors invest their money in bonds. On the other hand, when interest rates are low, investors invest in stocks, so stock prices are high.
Number of shares issued
The number of shares issued affects the stock price because the profit per share changes. Specifically, if the number of shares issued is large, the profit per share will be small and the stock price will be low. On the contrary, if the number of shares issued is small, the profit per share will be high and the stock price will be high.
Risk premium
The risk premium is the difference between the expected rate of return on risky assets minus the rate of return on risk-free assets. Stocks with high risk that may affect management tend to have lower stock prices.
How to use machine learning to predict stock prices
So what kind of method should we use to utilize machine learning for stock price forecasting? Here, we will introduce two typical methods.
Use a programming language
The first method is to use a programming language. At this time, “Python” is a typical programming language used for construction. Python is an open source programming language developed by a Dutch programmer named Guido van Rossum in 1991.
The advantage of using Python is that you can write a program concisely with a small amount of code, and you can have abundant specialized libraries. Also, because it is a very widely used language, there are plenty of learning sites and reference books. However, language knowledge and skills are essential for building machine learning using Python. Therefore, it can be said that the hurdle is high for beginners to challenge.
Use the framework
Using a framework is also an effective method. In particular, the machine learning method “LightGBM” based on gradient boosting released by Microsoft Corporation in 2016 is widely known as a recommended framework for stock price forecasting.
LightGBM has been devised in various ways to reduce the calculation time, and it is possible to build machine learning at a very high speed. In fact, it’s a popular framework with reported aggregated results that more than 60% of Kaggler’s use his LightGBM.
Take advantage of AI tools
Stock price forecasting using AI tools has the advantage of making investment decisions easier with proposals made by AI. In addition, it is a recommended tool for beginners as it can be used without specialized knowledge. Recently, AI tools that can be used not only for stock price forecasts but also for solving problems of various companies have appeared.
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summary
This time, we have summarized specific methods for making stock price forecasts using machine learning. It is said that automatic stock trading based on stock price forecasting using AI will become mainstream in the future. In addition, services that support the management of stocks for individual investors are also appearing, which is a very noteworthy field. Why don’t you try to predict the stock price by machine learning by referring to this article.
