Abstract: This research paper investigates the application of deep learning models, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, for predicting stock market prices. Utilizing historical data and trading volume information, our study aims to assess the performance and comparative effectiveness of LSTM and GRU architectures in capturing temporal dependencies and complex patterns inherent in financial time series data. Through comprehensive experimentation and evaluation, we analyze the predictive capabilities of both models and identify their strengths and limitations. Our findings contribute to advancing the understanding of deep learning techniques in financial forecasting, providing valuable insights for practitioners and researchers alike. By exploring the nuances between LSTM and GRU networks in stock market prediction, this study offers guidance for selecting appropriate models for future applications in financial markets.
Keywords: Stock Market, Deep Learning, LSTM, GRU, Finance.
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DOI:
10.17148/IJARCCE.2024.134164
