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Stock price prediction is a branch of financial forecasting that provides valuable insights for investors, traders, financial institutions, and other stakeholders. This study examines the performance of three deep learning algorithms—LSTM, GRU, and SimpleRNN—in forecasting stock prices based on past data. In contrast to research that emphasizes global efficiencies, this study assesses model performance according to dataset characteristics. The dataset, which includes daily stock prices from 2009 to 2019, focused on closing prices. Data preprocessing included Min-Max Scaling and sliding window methods. Hyperparameters such as batch size, learning rate, dropout rate, and L2 norm strength were modified, with the training and test sets making up 70%, 30% of the data, respectively. The performance of the model is assessed using Mean Squared Error (MSE), Mean Squared Percentage Error (MSPE), and Mean Absolute Error (MAE), indicating that LSTM and GRU excelled compared to SimpleRNN in understanding long-term dependencies. The document seeks to demonstrate the importance of adjusting the selection of models and hyperparameters in relation to the dataset’s features and outlines the theoretical frameworks for enhancing outcomes in financial time-series forecasting.

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Forecasting Indian Stock Market Prices Using Deep Learning Models

  • 会议论文

  • 发表日期：

    2026年

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  • Rajesh Kumar,
  • Suchi Kumari,
  • Kartikeya Pydi,
  • Laxmi Narayana Raavi

摘要

Stock price prediction is a branch of financial forecasting that provides valuable insights for investors, traders, financial institutions, and other stakeholders. This study examines the performance of three deep learning algorithms—LSTM, GRU, and SimpleRNN—in forecasting stock prices based on past data. In contrast to research that emphasizes global efficiencies, this study assesses model performance according to dataset characteristics. The dataset, which includes daily stock prices from 2009 to 2019, focused on closing prices. Data preprocessing included Min-Max Scaling and sliding window methods. Hyperparameters such as batch size, learning rate, dropout rate, and L2 norm strength were modified, with the training and test sets making up 70%, 30% of the data, respectively. The performance of the model is assessed using Mean Squared Error (MSE), Mean Squared Percentage Error (MSPE), and Mean Absolute Error (MAE), indicating that LSTM and GRU excelled compared to SimpleRNN in understanding long-term dependencies. The document seeks to demonstrate the importance of adjusting the selection of models and hyperparameters in relation to the dataset’s features and outlines the theoretical frameworks for enhancing outcomes in financial time-series forecasting.