عنوان مقاله / English Daily Stock Price Movement Prediction Using Sentiment text mining of social network and data mining of Technical indicators

Subject Areas : Journal of Investment Knowledge

Kamel Ebrahimian ¹ , ebrahim abbasi ² , Akbar Alam tabriz ³ , Amir Mohammadzadeh ⁴

1 - Phd-studentat Department of Management، Faculty of Management and Accounting، Qazvin Branch، Islamic Azad University، Qazvin، Iran
2 - Associate professor at AL-Zahra University.
3 - Professor at Department of Industrial Management، Management and Accounting Faculty، Shahid Beheshti University، Tehran، Iran.
4 - Department of Industrial at Department of Management، Islamic Azad University Qazvin، Qazvin، Iran.

Received: 2021-04-17 Accepted : 2021-04-24 Published : 2021-12-22

Keywords: Stock price forecasting, emotion analysis, Granger Causality, classification algorithms,

Abstract :

This study predicts the future movement of stock prices in the short term by using the analysis of investors' opinions on the social network. The predictability of stock markets, due to having a complex, dynamic and nonlinear system that it has always been one of the challenges for researchers. In this research, for the first time, we developed a model with 72.08%accuracy for predicting stock movement and predicting the trend by analyzing the feelings of users' opinions and combining it with 20 technical indicators and we use three data mining algorithms include decision tree, Naïve Bayes and Support Vector Machine. According to the results, the support vector machine showed better performance than the other algorithms. It was also found that the next day trading volume and the number of comments have a significant correlation and the results of Granger causality test showed can be used to predict stock price and also it took advantage of the aggregation of users' daily emotions.

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