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This paper proposed a combination of two different deep learning pattern recognition algorithms to generate buy/sell signals of sectoral stocks. The deep neural network training process involved filtering stock data based on technical indicators' values, and subsequent buying and selling signals were produced utilizing the forecast outcomes of these artificial neural networks. This approach utilized data sourced from the Indian stock market for specific sectors. The research discovered that a significant number of stocks tend to exhibit similar movement patterns. The main objective of this research is to devise a stock price forecasting aimed at identifying optimal moments for buying and selling signals.

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A Hybrid LSTM-CNN Model for Accurate Stock Price Prediction and Buy/Sell Signal Generation

  • 会议论文

  • 发表日期：

    2026年

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  • Chhaya Patel,
  • Ashwin Raiyani

摘要

This paper proposed a combination of two different deep learning pattern recognition algorithms to generate buy/sell signals of sectoral stocks. The deep neural network training process involved filtering stock data based on technical indicators' values, and subsequent buying and selling signals were produced utilizing the forecast outcomes of these artificial neural networks. This approach utilized data sourced from the Indian stock market for specific sectors. The research discovered that a significant number of stocks tend to exhibit similar movement patterns. The main objective of this research is to devise a stock price forecasting aimed at identifying optimal moments for buying and selling signals.