Article: Enhanced stock price prediction with a CNN-BiLSTM deep learning approach optimised by genetic algorithms Journal: International Journal of Intelligent Enterprise (IJIE) 2026 Vol.13 No.2 pp.171 - 194 Abstract: People's interest in the stock market has increased in recent years, as economic growth has increased. Accurate predictions of stock price fluctuations provide more financial gain while minimising risk. However, forecasting is challenging due to the constant fluctuations in pricing and their frequently uncertain movements. We propose a hybrid model that combines convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM) networks, and a genetic algorithm (GA) for stock price prediction. This work compares our model with five other models: CNN, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM. We have used three evaluation metrics to evaluate each model's performance. The proposed GA-CNN-BiLSTM model enhances overall performance by effectively optimising hyperparameters. We analysed a 10-year Nifty 50 dataset and found that the proposed model consistently outperforms baseline models. We conducted additional experiments on global index datasets, including the S&P 500 and Nikkei 225, to assess the model's robustness and significance. Inderscience Publishers - linking academia, business and industry through research

Title: Enhanced stock price prediction with a CNN-BiLSTM deep learning approach optimised by genetic algorithms

Authors: Rajesh Kumar Ghosh; Bhupendra Kumar Gupta; Srikanta Patnaik; Ajit Kumar Nayak

Addresses: Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Information System and Management, Interscience Institute of Management and Technology, Bhubaneswar, Odisha, India ' Department of Computer Science and Information Technology, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

Abstract: People's interest in the stock market has increased in recent years, as economic growth has increased. Accurate predictions of stock price fluctuations provide more financial gain while minimising risk. However, forecasting is challenging due to the constant fluctuations in pricing and their frequently uncertain movements. We propose a hybrid model that combines convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM) networks, and a genetic algorithm (GA) for stock price prediction. This work compares our model with five other models: CNN, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM. We have used three evaluation metrics to evaluate each model's performance. The proposed GA-CNN-BiLSTM model enhances overall performance by effectively optimising hyperparameters. We analysed a 10-year Nifty 50 dataset and found that the proposed model consistently outperforms baseline models. We conducted additional experiments on global index datasets, including the S&P 500 and Nikkei 225, to assess the model's robustness and significance.

Keywords: convolutional neural network; CNN; bidirectional long short‑term memory; BiLSTM; deep learning; genetic algorithm; technical indicators; stock price prediction.

DOI: 10.1504/IJIE.2026.152975

International Journal of Intelligent Enterprise, 2026 Vol.13 No.2, pp.171 - 194

Received: 04 Sep 2024
Accepted: 15 Aug 2025
Published online: 17 Apr 2026 *

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Title: Enhanced stock price prediction with a CNN-BiLSTM deep learning approach optimised by genetic algorithms

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