[4Rin1-72] Construction and Analysis of News Evaluation Model for Electric Manufacturers Using Natural Language Generation through GPT-2
Keywords:Natural language generation, GPT-2, Deep learning, LSTM, News Evaluation
News articles distributed in financial markets are essential information that affects asset valuation. Many studies have analyzed the impact of delivered news articles on stock price fluctuations. However, the number of news articles is limited, and the limit on the number of data that can be obtained affects the accuracy of analysis using deep learning. In this study, we constructed a news evaluation model based on stock price fluctuation before and after news article distribution and tried to improve the accuracy of the model by generating news articles through GPT-2. The analysis target was a Japanese electronics manufacturer, and analysis was performed using a news evaluation model. As a result of the analysis, we found that there is a possibility that the generation of news articles through GPT-2 can improve the accuracy of the news evaluation model.
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