Abstract
This study constructs a text analysis system for analyzing financial markets. This system enables us to investigate the influence of news article on intraday price changes. In this study, we examine the automobile companies in Japan to analyze the relationship between news articles and stock price reactions. As a result of empirical analyses, we confirmed that stock prices reflect news information in a timely manner. These results are suggestive from both academic and practical view points. More detailed analyses are planned for the future.
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Notes
- 1.
- 2.
In this study, we selected Support Vector Regression with the linear kernel as a supervised learning. But our proposed system can work with other kinds of supervised learning.
- 3.
In procedures 1 and 2, we analyze all Japanese news articles in relation to the Japanese stock market during the sample periods.
- 4.
Although, we don’t employ a deep learning model in this study, in another article [10], we employ a recursive neural network model to analyze the relationship between news and stock indices. Detailed analysis using both a recursive neural network model and intraday price changes is one of our future plans.
- 5.
These companies are ranked in the top three companies in the automobile sector in Japan.
- 6.
- 7.
\(\mathrm{Time}=0\) in x-axis shows the point when news articles are released to investors.
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This research was supported by a grant-in-aid from the Telecommunications Advancement Foundation.
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Goshima, K., Takahashi, H. (2016). Text Analysis System for Measuring the Influence of News Articles on Intraday Price Changes in Financial Markets. In: Jezic, G., Chen-Burger, YH., Howlett, R., Jain, L. (eds) Agent and Multi-Agent Systems: Technology and Applications. Smart Innovation, Systems and Technologies, vol 58. Springer, Cham. https://doi.org/10.1007/978-3-319-39883-9_28
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