Presentation information

General Session

General Session » GS-10 AI application

[1F3-GS-10b] AI応用:経営情報

Tue. Jun 8, 2021 3:20 PM - 5:00 PM Room F (GS room 1)

座長:松井 孝太(名古屋大学)

4:40 PM - 5:00 PM

[1F3-GS-10b-05] Extraction of sentences of forecasted business performance containing causal information from summaries of financial statements

〇Kohei Kawamura1, Kaito Takano1, Hiroyuki Sakai1 (1. Seikei University)

Keywords:forecasted business performance, causal information, investment support, text mining, information extraction

Stock prices often move with future performance forecasts rather than past performance, and even if the current performance is in the red, the stock price may rise if the company shows that the company's performance will recover. Therefore, performance forecast information is important for investment decisions. In this paper, we propose a method to extract forecasted business performance (statements that describe future performance forecasts of companies) from summaries of financial statements as a support for individual investors' investment decisions. In particular, we focus on forecasted business performance containing performance factors. Our method is able to generate highly accurate training data automatically, and by using the pre-trained BERT as the classification model, a relatively good result was obtained with an F1-score of about 83.6.

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