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The article deals with creating an information strategic system with a deep neural network in investments by investors and researchers for predicting stock prices of the NIFTY50 index for the Indian stock market. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks were used. This network architecture was chosen because both are good at capturing the patterns of time-series data, collected in the stock market. More than twenty models were created and then compared for their performance. LSTM has been observed to have higher performance than GRU, and both models are very good at predicting stock market data.

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Stock Market Values Prediction Using Deep Neural Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Ivanna Dronyuk,
  • Myroslava Klapchuk,
  • Deep Shankar Pratap Singh

摘要

The article deals with creating an information strategic system with a deep neural network in investments by investors and researchers for predicting stock prices of the NIFTY50 index for the Indian stock market. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks were used. This network architecture was chosen because both are good at capturing the patterns of time-series data, collected in the stock market. More than twenty models were created and then compared for their performance. LSTM has been observed to have higher performance than GRU, and both models are very good at predicting stock market data.