Babak Sohrabi - Prof., Department of Information Technology Management, Faculty of management, University of Tehran, Tehran, Iran.
Saeed Rouhani - Associate Prof., Department of Information Technology Management, College of Management, University of Tehran, Tehran, Iran.
Hamid Reza Yazdani - Associate Prof., Department of Business Management, Farabi Collage, University of Tehran, Tehran, Iran.
Ahmad Khalili Jafarabad - Ph.D., Department of Information Technology Management, Faculty of Management, University of Tehran, Tehran, Iran.
Mahsima Kazemi Movahed - Ph.D. Candidate, Department of Information Technology Management, Faculty of management, University of Tehran, Tehran, Iran.

Two predominant methods for analyzing financial markets have been technical and fundamental analysis. However, the emergence of the Internet has altered the trading landscape. The availability of Internet and social media access plays a moderating role in information asymmetry, resulting in investors making informed decisions. Social media has turned into a source of information for investors. Through diverse communication channels on social media, investors articulate their perspectives on whether to buy or sell a stock. According to Surowiecki, the collective opinions gathered through social media frequently offer better predictions than individual opinions, a phenomenon referred to as the Wisdom of the Crowd. The wisdom of the crowd stands as an essential measure within social networks, with its potential to reduce errors and lessen information-gathering costs. In this study, we tried to evaluate the wisdom of the crowd's potential to improve stock price prediction accuracy. So, we developed a prediction model by Long Short-Term Memory based on the wisdom of the crowd. Users’ opinions in Persian about the Tehran Stock Exchange (TSE) stocks were collected from SAHMETO for eight months. The Support Vector Machine classified them into buy, sell, and neutral classes. During the research period, people mentioned ۸۲۳ stocks, and ۵۲ stocks with over ۱۰۰ signals were chosen. The results of the study show that although the model presented has achieved an acceptable level of accuracy, correlations between the actual and predicted values exceeded ۹۰%. The accuracy metrics of the proposed model compared to the base model were not improved.

Wisdom of Crowd, Stock Price Prediction, Long Short-Term Memory, LSTM