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**1. Introduction**
Recent advances in AI technology have led to the development of models that analyze real-time news and stock price data to predict stock price movements. These models can help investors predict stock price movements more accurately, and many financial institutions have developed and utilized AI-based stock price prediction models.
**2. Data Collection**
First, to develop a stock price prediction model, you need to collect real-time news and stock price data from various sources. News data can be collected from major news sites or financial information providers, and stock price data can be collected from stock exchanges or financial information providers. These data are updated hourly, so they can be collected and analyzed in real time.
**3. Data preprocessing**
Data preprocessing is necessary to apply the collected data to the model. In the case of text data, natural language processing techniques can be used to analyze the content of news articles and extract important information. Since stock price data is time series data, you can apply time series analysis techniques to analyze trends and periodicity.
**4. Model development**
Next, you need to develop an AI model based on the data you have collected. The most commonly used models are artificial neural networks (Deep Learning)
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