文章基本信息

标题：The Impact of Persian News on Stock Returns Through Text Mining Techniques
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作者：Zahra Azizi ; Neda Abdolvand ; Hassan Ghalibaf Asl 等
期刊名称：Iranian Journal of Management Studies
印刷版ISSN：2008-7055
出版年度：2021
卷号：14
期号：4
页码：799-816
DOI：10.22059/ijms.2021.295478.673915
语种：English
出版社：University of Tehran * College of Farabi
摘要：The news contains information about the fundamentals of the company and can change the behavior of the stock market. However, most research in stock market prediction has relied on technical analysis, i.e., time series analysis, based on past stock data, and the impact of fundamental data – especially Persian news – on the stock prices has been neglected. Consequently, this study aimed to fill this gap. To this aim, the stock index values were collected from the Tehran Stock Exchange along with the news published during this period. Then, the semantic load of news sentences was determined using text mining and sentiments analysis techniques, and the news was classified into positive and negative categories using machine-learning algorithms. Finally, the relationship between news and stock index was evaluated using logistic regression. According to the results, published news has a positive or negative semantic burden, and is effective on the index value.
关键词：Stock market index;Stock market prediction;Persian news;Text mining;Sentiment analysis;Technical and fundamental data