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All Journal International Journal of Electrical and Computer Engineering
Yesmukhanova, Dariga
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Computer Science & IT Electrical & Electronics Engineering

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Forecasting stock market prices using deep learning methods Ismailova, Aisulu; Beldeubayeva, Zhanar; Kadirkulov, Kuanysh; Doumcharieva, Zhanagul; Konyrkhanova, Assem; Ussipbekova, Dinara; Aripbayeva, Ainura; Yesmukhanova, Dariga
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5601-5611

Abstract

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 GRU's 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.
Co-Authors Aripbayeva, Ainura Beldeubayeva, Zhanar Doumcharieva, Zhanagul Ismailova, Aisulu Kadirkulov, Kuanysh Konyrkhanova, Assem Ussipbekova, Dinara