Analysis of Long-term Market Trend by Text-Mining of News Articles
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- Kuramoto Takahisa
- School of Engineering, The University of Tokyo
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- Izumi Kiyoshi
- School of Engineering, The University of Tokyo CREST, PRESTO, JST
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- Shinobu Yoshimura
- School of Engineering, The University of Tokyo
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- Ishida Tomonari
- Nomura Securities Co., Ltd
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- Nakashima Akihiro
- Nomura Securities Co., Ltd
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- Matsui Tohgoroh
- College of Life and Health Sciences , Chubu University
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- Yoshida Minoru
- Information Technology Center, The University of Tokyo
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- Nakagawa Hiroshi
- Information Technology Center, The University of Tokyo
Bibliographic Information
- Other Title
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- 新聞記事のテキストマイニングによる長期市場動向の分析
Description
In this study, we developed a new method of the long-term market analysis by using text-mining of news articles. Using our method, we conducted extrapolation tests to predict stock price averages by 19 industry and two market averages, TOPIX and Nikkei225 for about 10 years. As a result, 8 sectors in 21 sectors (about 40%) showed over about 60% accuracy, and 15 sectors in 21 sectors (over 70%) showed over about 55% accuracy. We also developed a web system of financial text-mining based on our method for financial professionals.
Journal
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- Transactions of the Japanese Society for Artificial Intelligence
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Transactions of the Japanese Society for Artificial Intelligence 28 (3), 291-296, 2013
The Japanese Society for Artificial Intelligence
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Keywords
Details 詳細情報について
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- CRID
- 1390282680085059968
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- NII Article ID
- 130003362333
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- BIBCODE
- 2013TJSAI..28..291K
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- ISSN
- 13468030
- 13460714
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- Text Lang
- ja
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- Article Type
- journal article
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
- OpenAIRE
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- Abstract License Flag
- Disallowed
