Forecasting stock market prices using deep learning methods
Ismailova A. Beldeubayeva Z. Kadirkulov K. Doumcharieva Z. Konyrkhanova A. Ussipbekova D. Aripbayeva A. Yesmukhanova D.
October 2024Institute of Advanced Engineering and Science
International Journal of Electrical and Computer Engineering
2024#14Issue 55601 - 5611 pp.
The article focuses on enhancing stock market price prediction through artificial neural networks and machine learning. It underscores the significance of improving forecast accuracy by incorporating historical stock prices, macroeconomic indicators, news events, and technical indicators. Exploring deep learning principles, it delves into convolutional neural networks (CNN), recurrent neural networks (RNN), including long short-term memory (LSTM), and gated recurrent unit (GRU) modifications. This financial time series processing study covers data preprocessing, creating training/test sets, and selecting evaluation metrics. Results suggest promising applications for the developed forecasting models in stock markets, stressing the importance of considering various factors for precise forecasts in dynamic financial environments. Historical reserve data serves as the model foundation. Integration of macroeconomic, news, and technical indicators offers a holistic approach, aiding trend and anomaly identification for enhanced forecasts. The article recommends suitable deep learning architectures, highlighting LSTM and GRUs effectiveness in adapting to intricate data dependencies. Experimental outcomes showcase these architectures benefits in predicting stock market prices, offering valuable insights for finance and asset management professionals in financial analysis and machine learning realms.
Deep learning , Financial analysis , Forecasting stock , Gated recurrent unit , Long short-term memory , Stock market
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Department of Information Systems, S. Seifullin Kazakh AgroTechnical Research University, Astana, Kazakhstan
Department of Applied Informatics and Programming, Taraz Regional University named after M.Hh. Dulati, Taraz, Kazakhstan
Department of Information Security, Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Information and Communication Technologies, Non-profit Joint Stock Company S. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
Department of Biostatistics, Bioinformatics and Information Technologies, Astana Medical University, Astana, Kazakhstan
Department of Natural Sciences, West Kazakhstan Medical University named after Marat Ospanov, Aktobe, Kazakhstan
Department of Information Systems
Department of Applied Informatics and Programming
Department of Information Security
Department of Information and Communication Technologies
Department of Biostatistics
Department of Natural Sciences
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