{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T09:51:02Z","timestamp":1687600262764},"reference-count":23,"publisher":"IGI Global","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,10,1]]},"abstract":"<p>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 \u2013 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.<\/p>","DOI":"10.4018\/ijbir.2013100101","type":"journal-article","created":{"date-parts":[[2014,4,1]],"date-time":"2014-04-01T17:47:06Z","timestamp":1396374426000},"page":"1-21","source":"Crossref","is-referenced-by-count":2,"title":["Correlation between the Economy News and Stock Market in Turkey"],"prefix":"10.4018","volume":"4","author":[{"given":"Sadi Evren","family":"Seker","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Istanbul University, Istanbul, Republic of Turkey"}]},{"given":"Cihan","family":"Mert","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Texas at Dallas, TX, USA"}]},{"given":"Khaled","family":"Al-Naami","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Texas at Dallas, TX, USA"}]},{"given":"Nuri","family":"Ozalp","sequence":"additional","affiliation":[{"name":"Turkish Science Foundation, Istanbul, Republic of Turkey"}]},{"given":"Ugur","family":"Ayan","sequence":"additional","affiliation":[{"name":"Turkish Science Foundation, Istanbul, Republic of Turkey"}]}],"member":"2432","reference":[{"key":"ijbir.2013100101-0","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-5915.1987.tb01533.x"},{"key":"ijbir.2013100101-1","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/3-540-47887-6_48","article-title":"News sensitive stock trend prediction.","volume":"233","author":"G. 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H.Tan","journal-title":"Erasmus University Rotterdam."},{"key":"ijbir.2013100101-19","author":"W.Trappe","year":"2006","journal-title":"Introduction to Cryptography with Coding Theory"},{"key":"ijbir.2013100101-20","doi-asserted-by":"publisher","DOI":"10.1109\/64.2091"},{"key":"ijbir.2013100101-21","doi-asserted-by":"crossref","unstructured":"Wuthrich, B., Cho, V., Leung, S., Permunetilleke, D., Sankaran, K., & Zhang, J. (1998). Daily stock market forecast from textual web data. SMC98 Conference Proceedings 1998 IEEE International Conference on Systems Man and Cybernetics Cat No98CH36218. 3, pp. 2720-2725. Ieee.","DOI":"10.1109\/ICSMC.1998.725072"},{"key":"ijbir.2013100101-22","unstructured":"Yahia, M. E., & Ibrahim, B. (2003). K-nearest neighbor and C4.5 algorithms as data mining methods: advantages and difficulties. Computer Systems and Applications, 2003. Book of Abstracts. ACS\/IEEE International Conference on, (p. 103). 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