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Correlation between the Economy News and Stock Market in Turkey

Correlation between the Economy News and Stock Market in Turkey

Sadi Evren Seker, Cihan Mert, Khaled Al-Naami, Nuri Ozalp, Ugur Ayan
Copyright: © 2013 |Volume: 4 |Issue: 4 |Pages: 21
ISSN: 1947-3591|EISSN: 1947-3605|EISBN13: 9781466635173|DOI: 10.4018/ijbir.2013100101
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MLA

Seker, Sadi Evren, et al. "Correlation between the Economy News and Stock Market in Turkey." IJBIR vol.4, no.4 2013: pp.1-21. http://doi.org/10.4018/ijbir.2013100101

APA

Seker, S. E., Mert, C., Al-Naami, K., Ozalp, N., & Ayan, U. (2013). Correlation between the Economy News and Stock Market in Turkey. International Journal of Business Intelligence Research (IJBIR), 4(4), 1-21. http://doi.org/10.4018/ijbir.2013100101

Chicago

Seker, Sadi Evren, et al. "Correlation between the Economy News and Stock Market in Turkey," International Journal of Business Intelligence Research (IJBIR) 4, no.4: 1-21. http://doi.org/10.4018/ijbir.2013100101

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Abstract

Depending on the market strength and structure, it is a known fact that there is a correlation between the stock market values and the content in newspapers. The correlation increases in weak and speculative markets, while they never get reduced to zero in the strongest markets. This research focuses on the correlation between the economic news published in a highly circulating newspaper in Turkey and the stock market closing values in Turkey. In the research several feature extraction methodologies are implemented on both of the data sources, which are the stock market values and economic news. Since the economic news is in natural language format, the text mining technique, term frequency – inverse document frequency is implemented. On the other hand, the time series analysis methods like random walk, Bollinger band, moving average or difference are applied over the stock market values. After the feature extraction step, the classification methods are built on the well-known classifiers support vector machine, k-nearest neighborhood and decision tree. Moreover, an ensemble classifier based on majority voting is implemented on top of these classifiers. The success rates show that the results are satisfactory to claim the methods implemented in this study can be spread to future research with similar data sets from other countries.

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