Presentation information

Oral presentation

General Session » [General Session] 3. Data Mining

[2O4] [General Session] 3. Data Mining

Wed. Jun 6, 2018 5:20 PM - 7:00 PM Room O (2F Kaimon)

座長:大知 正直(東京大学)

6:20 PM - 6:40 PM

[2O4-04] Extraction of Causal Knowledge from Annual Securities Report

〇Fumihito Sato1, Hiroaki Sakuma1, Shunya Kodera1, Yoshinori Tanaka1, Hiroki Sakaji2, Kiyoshi Izumi2 (1. Nikko Research Center, Inc., 2. Graduate School of Engineering, The University of Tokyo)

Keywords:Annual Securities Report, Text Mining, Machine Learning , Causal Knowledge

In annual securities report, various information such as corporate policy, risk management, R&D, and so on, is included other than business performance. Previous researches proposed the extraction methods of important sentences containing causal information from financial articles and texts but not annual financial reports. In this paper, we applied these extracting methods based on SVM discriminant model to annual securities reports in our original way. Our method indicated high performance and all evaluations, that were precision, recall and F-score, showed more than 0.8. By using our model, useful information from annual securities reports would be collected effectively, which allow us to make unique investment decisions.