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The Importance of Developing a Stock Price Movement Prediction Model
Predicting a company’s stock price movement is crucial in a volatile financial market. It enables investors to make profits by leading the market, and companies to build efficient capital markets. Therefore, research is needed to improve the accuracy of stock price prediction models using AI.
Data collection and preprocessing
The performance of stock price prediction models is highly dependent on the quality of data. Therefore, when collecting data, it is necessary to comprehensively utilize various data such as stock prices, corporate financial information, and economic indicators. In addition, data preprocessing should handle missing values and outliers, and normalize the data to improve the learning performance of the model.
Using various AI algorithms
A stock price prediction model using AI can be built using various algorithms. Some of the most commonly used algorithms include Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and AutoRegressive Integrated Moving Average (ARIMA). These different algorithms can be used to increase the accuracy of the model.
Hyperparameter tuning
Tuning hyperparameters is necessary to improve the performance of the model.
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