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Construction of a news article evaluation model utilizing high-frequency data and a large-scale language generation model

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Abstract

News articles have significant impacts on asset prices in financial markets. A great number of attempts have been conducted to ascertain how news articles influence stock prices. News articles have been reported to contain sentimental and fundamental information that affects stock price fluctuations, and many studies have been conducted to evaluate stock price fluctuations using them as analytical data. However, the limitations in the number of available datasets usually become the hurdle for the model accuracy. This study aims to improve the analytical model’s accuracy by generating news articles using language generation technology. We tested whether the model that used the generated data was better than the trained model with real-world data. The model constructed in this research is a model that evaluates news articles distributed to financial markets based on the price fluctuation rate of stock prices and predicts and evaluates stock price fluctuations. This study labeled based on high-frequency trading data and generated news articles using a large-scale language generation model (GPT-2). Also, we analyzed and verified the effect. In this study, we succeeded in generating news articles using the large-scale language generation model and improving the classification accuracy. Our method proposed in this paper has great potential to improve text analysis accuracy in various areas.

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Data availability

Data available from Thomson Reuters Real-Time News: Feed and Archive, Nikkei Needs and Refinitiv Eikon. The authors do not have permission to share data.

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Acknowledgements

This study extends a previous study (Nishi et al. 2020b) and builds on a more accurate classification model through high-frequency data and sentence generation models.

Funding

Partial funding by JSPS KAKENHI, JP20K01751.

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Authors and Affiliations

  1. Graduate School of Business Administration, Keio University, Hiyoshi 4-1-1, Kohoku-ku, Yokohama-shi, Kanagawa-ken, Japan

    Yoshihiro Nishi, Aiko Suge & Hiroshi Takahashi

Authors
  1. Yoshihiro Nishi
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  2. Aiko Suge
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  3. Hiroshi Takahashi
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Correspondence to Yoshihiro Nishi.

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Nishi, Y., Suge, A. & Takahashi, H. Construction of a news article evaluation model utilizing high-frequency data and a large-scale language generation model. SN Bus Econ 1, 104 (2021). https://doi.org/10.1007/s43546-021-00106-0

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