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人工知能学会論文誌
Vol. 25 (2010) No. 3 P 383-387

記事言語:

http://doi.org/10.1527/tjsai.25.383

速報論文

In this study, we proposed a new text-mining methods for long-term market analysis. Using our method, we analyzed monthly price data of financial markets; Japanese government bond market, Japanese stock market, and the yen-dollar market. First we extracted feature vectors from monthly reports of Bank of Japan. Then, trends of each market were estimated by regression analysis using the feature vectors. As a result, determination coefficients were over 75%, and market trends were explained well by the information that was extracted from textual data. We compared the predictive power of our method among the markets. As a result, the method could estimate JGB market best and the stock market is the second.

Copyright © 2010 JSAI (The Japanese Society for Artificial Intelligence)

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