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Stock Price Prediction Based on Shareholders Trading Behavior

Masoud, Mahsa | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56450 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Habibi, Jafar
  7. Abstract:
  8. Nowadays, the capital market has a significant impact on the economy of a country and causes economic dynamism and growth in gross production. Among the important phenomena in the stock market is stock pricing, the correctness or incorrectness of which has a significant role in the performance of the stock market and the value of companies. The stock price in the stock exchange represents the stock market value and usually represents the investment value of the shareholders. Forecasting the trend of the stock market is considered an important and necessary thing and has been given much attention, because the successful forecasting of the stock price may lead to attractive profits by making the right decisions. Many factors, including financial reports of companies, changes in economic policies, and the prevailing atmosphere in society, play a role in changing the price of shares in the market. The stock price is influenced by several factors, including the activity of shareholders. Shareholder activities can have a significant impact on a company's stock price, and investors need to be aware of these factors when making investment decisions. Deep learning models have been widely studied in recent years, neural networks play an important role in stock price prediction in the stock market by providing a powerful tool for modeling complex relationships and patterns in stock price data. In this research, we presented a model that predicts the closing price of the stock in the coming days using historical stock data as well as the trading behavior of the shareholders of this stock over time. This model was trained using deep neural networks and meta-revelation learning. Experiments on real data sets show that the presented method will give a significant improvement over the methods presented in previous research
  9. Keywords:
  10. Deep Neural Networks ; Stock Prediction ; Financial Time Series ; Meta Heuristic Algorithm ; Metalearning ; Investor Trading Behaviors ; Trading Behavior

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