Abstract
This study constructed a news article evaluation system that utilizes a language generation model to analyze financial markets. This system enables us to analyze the effect of news articles distributed in financial markets on the stock price of a company. We added the generated news articles as data for analysis through GPT-2 and verified the accuracy of the constructed system. As a result of empirical analyses, we confirmed that the accuracy of the model with the generated news articles improved. More detailed analyses are planned for the future.
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In this study, the positive news and negative news are based on stock price fluctuations and are not regarding emotional polarity values.
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Nishi, Y., Suge, A., Takahashi, H. (2020). Construction of News Article Evaluation System Using Language Generation Model. In: Jezic, G., Chen-Burger, J., Kusek, M., Sperka, R., Howlett, R., Jain, L. (eds) Agents and Multi-Agent Systems: Technologies and Applications 2020. Smart Innovation, Systems and Technologies, vol 186. Springer, Singapore. https://doi.org/10.1007/978-981-15-5764-4_29
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DOI: https://doi.org/10.1007/978-981-15-5764-4_29
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