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

Rajesh Kumar Ghosh, Bhupendra Kumar Gupta, Srikanta Patnaik and Ajit Kumar Nayak

International Journal of Intelligent Enterprise, 2026, vol. 13, issue 2, 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.

Keywords: convolutional neural network; CNN; bidirectional long short‑term memory; BiLSTM; deep learning; genetic algorithm; technical indicators; stock price prediction. (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.inderscience.com/link.php?id=152975 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijient:v:13:y:2026:i:2:p:171-194

Access Statistics for this article

More articles in International Journal of Intelligent Enterprise from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().