“A study of how AI can be used to improve the accuracy and reliability of models that predict the stock prices of specific companies.”


(adsbygoogle = window.adsbygoogle || []).push({});

**1. Research Background**

Recently, various models using AI technology have been developed to predict stock prices of companies, but the accuracy and stability of these models are often still insufficient. Therefore, this study was conducted to derive a way to improve the accuracy and stability of the model that predicts the stock price of a specific company using AI.

**2. Data Collection and Preprocessing**

First, we collected various data related to the stock price of the specific company under study and preprocessed them. In addition to stock price data, various factors such as company financial information, industry trends, and management information were comprehensively considered and utilized as input data for the model.

**3. Model development**

Next, an AI model was developed based on the data. In this study, we mainly used deep learning algorithms to build the model, and designed a model that processes time series data and image data by utilizing LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network). In addition, we also conducted experiments to improve the stability of the model by combining Reinforcement Learning.

**4. Model Evaluation and Performance Improvement**

After training the model, we evaluated various

(adsbygoogle = window.adsbygoogle || []).push({});