The Impact of Persian News on Stock Returns Through Text Mining Techniques
Publish place: Iranian Journal of Management Studies، Vol: 14، Issue: 4
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JIJMS-14-4_008
تاریخ نمایه سازی: 23 شهریور 1401
Abstract:
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.
Keywords:
Stock Market Index , Stock market prediction , Persian News , Text mining , Sentiment analysis , Technical and fundamental data
Authors
زهرا عزیزی
Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran
ندا عبدالوند
Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran
حسن قالیباف اصل
Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran
سعیده رجائی هرندی
Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran
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