Construction and Analysis of News Evaluation Model for Electric Manufacturers Using Natural Language Generation through GPT-2

DOI

Bibliographic Information

Other Title
  • GPT-2を介した自然言語生成を用いた電機メーカーのニュース評価モデル構築と分析

Description

<p>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.</p>

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Keywords

Details 詳細情報について

  • CRID
    1390848250119825792
  • NII Article ID
    130007857394
  • DOI
    10.11517/pjsai.jsai2020.0_4rin172
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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