Forecasting Price and Trading Volume in Tehran Stock Market Using Data Mining in Telegram Channels

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Zohreei, Parsa | 2022

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 55285 (44)
University: Sharif University of Technology
Department: Management and Economics
Advisor(s): Zamani, Shiva
Abstract:
In inefficient stock markets, fast and complete access to the public information about stocks and using this information for trades can make the investment more profitable. This research gathered the Iranian telegram channel's data with stock and investment subjects, trading volume, and stock returns. We suggested a trading strategy for beating the market by processing these data. We have also investigated the transaction costs in this research.
Keywords:
Stock Market ; Market Efficiency ; Social Networks ; Machine Learning ; Burse ; Shares ; Stock Prediction ; Tehran Stock Exchange

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