Financial markets have always been under influence of media news; therefore, text analysis of news is considered as an effective method of stock exchange forecasting. Research in this context has been conducted with the help of information retrieval techniques, in which
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Financial markets have always been under influence of media news; therefore, text analysis of news is considered as an effective method of stock exchange forecasting. Research in this context has been conducted with the help of information retrieval techniques, in which high frequency words in a document that appeared sporadically in the whole corpus received higher weight than others. In contrast, the words which appeared in many news of a corpus, during a certain time, indicate the importance of an event. In our research, to address this contradiction, a new technique of assigning weight to influential words of news is presented. Financial news of Iran Central Bank (CBI) and actual data of Tehran Stock Exchange Index (TSEI) in the duration of 2005 to 2020 AD were utilized to evaluate the proposed method. The empirical results show 64% and 41% accuracy of trend prediction when TSEI moves upward and downward respectively and about 10% decreasing in Mean Absolute Error (MAE) to compare with prevalent techniques. While, the changes of the ratio between the number of positive and negative words in news does not offer predictive or analytical evidences, our results show that, there still exists a meaningful relationship between CBI news and TSEI fluctuations.
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