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This paper proposes an information retrieval method for the economy news. Theeffect of economy news, are researched in the word level and stock marketvalues are considered as the ground proof. The correlation between stock marketprices and economy news is an already addressed problem for most of thecountries. The most well-known approach is applying the text mining approachesto the news and some time series analysis techniques over stock market closingvalues in order to apply classification or clustering algorithms over thefeatures extracted. This study goes further and tries to ask the question whatare the available time series analysis techniques for the stock market closingvalues and which one is the most suitable? In this study, the news and theirdates are collected into a database and text mining is applied over the news,the text mining part has been kept simple with only term frequency-inversedocument frequency method. For the time series analysis part, we have studied10 different methods such as random walk, moving average, acceleration,Bollinger band, price rate of change, periodic average, difference, momentum orrelative strength index and their variation. In this study we have alsoexplained these techniques in a comparative way and we have applied the methodsover Turkish Stock Market closing values for more than a 2 year period. On theother hand, we have applied the term frequency-inverse document frequencymethod on the economy news of one of the high-circulating newspapers in Turkey.

Time Series Analysis on Stock Market for Text Mining Correlation of Economy News