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Research on stock price prediction models
Predicting the future direction of stock prices by utilizing historical stock price data is a topic of interest to many investors. Recently, researchers have been actively developing stock price prediction models using machine learning and deep learning, which are artificial intelligence techniques. Let’s take a look at the research on stock price prediction models:
Data collection
To train a stock price prediction model, you first need to collect stock price data of various stocks. Stock price data contains past stock price movements, and stock price movements are affected by many factors. Therefore, you need to collect data by considering the environmental factors of the stock market.
Preprocess the data
Before analyzing the stock price data you have collected, you need to preprocess the data properly. Stock price data may contain missing values or outliers, which can adversely affect the performance of your model if not handled. It is also important to normalize the stock price data to make it easier for the model to learn.
Train the AI model
Based on the preprocessed data, train a stock price prediction model using machine learning or deep learning algorithms. At this point
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