Predicting Stock Market Trends Using LSTM Networks: Overcoming RNN Limitations for Improved Financial Forecasting

Main Article Content

Jiajing Wang
Siyi Hong
Yuxin Dong
Zichao Li
Jinxin Hu

Abstract

In recent years, stocks have increasingly attracted our attention. The inherent volatility of stock prices, often caused by national and social policies, makes it challenging for investors to achieve profitable returns in the stock market. With the rapid advancement of artificial intelligence, computers have become adept at handling complex mathematical problems. Consequently, efforts have been made to leverage computers' remarkable computational capabilities to analyze and predict stock market trends. A growing number of professionals are delving into technologies related to deep learning. Two prominent applications in this field are data classification and regression. Recurrent Neural Networks (RNNs) have demonstrated superior performance in processing sequential data compared to other neural networks. However, RNNs can face issues such as gradient explosion or gradient vanishing when handling large datasets. These problems can cause RNNs to forget earlier data, resulting in inaccurate predictions. To address these issues, the Long Short-Term Memory (LSTM) model, an enhanced version of RNN, was introduced. LSTM incorporates input gates, forget gates, and output gates, effectively mitigating data forgetting and gradient explosion problems. By harnessing the computational power of computers, it becomes possible to make informed predictions about stock movements.

Article Details

How to Cite
Wang, J., Hong, S., Dong, Y., Li, Z., & Hu, J. (2024). Predicting Stock Market Trends Using LSTM Networks: Overcoming RNN Limitations for Improved Financial Forecasting. Journal of Computer Science and Software Applications, 4(3), 1–7. https://doi.org/10.5281/zenodo.12200708
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Articles

References

Gurav, U., & Sidnal, N. (2018). Predict stock market behavior: role of machine learning algorithms. In Intelligent Computing and Information and Communication: Proceedings of 2nd International Conference, ICICC 2017 (pp. 383-394). Springer Singapore.

Liu, Z., Wu, M., Peng, B., Liu, Y., Peng, Q., & Zou, C. (2023, July). Calibration Learning for Few-shot Novel Product Description. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1864-1868).

Hong, S. (2021, December). Multi-task Learning Based on Multiple Data Sources for Cancer Detection. In 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI) (pp. 486-491). IEEE.

Zi, Y., Wang, Q., Gao, Z., Cheng, X., & Mei, T. (2024). Research on the application of deep learning in medical image segmentation and 3d reconstruction. Academic Journal of Science and Technology, 10(2), 8-12.

Wang, W., Gao, M., Xiao, M., Yan, X., & Li, Y. (2024). Breast Cancer Image Classification Method Based on Deep Transfer Learning. arXiv preprint arXiv:2404.09226.

Hu, Y., Hu, J., Xu, T., Zhang, B., Yuan, J., & Deng, H. (2024). Research on early warning model of cardiovascular disease based on computer deep learning. arXiv preprint arXiv:2406.08864

Xiao, M., Li, Y., Yan, X., Gao, M., & Wang, W. (2024). Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example. arXiv preprint arXiv:2404.08279.

Song, J., & Liu, Z. (2021, November). Comparison of Norm-Based Feature Selection Methods on Biological Omics Data. In Proceedings of the 5th International Conference on Advances in Image Processing (pp. 109-112).

Sun, D., Liang, Y., Yang, Y., Ma, Y., Zhan, Q., & Gao, E. (2024). Research on optimization of natural language processing model based on multimodal deep learning. arXiv preprint arXiv:2406.08838

Mei, T., Zi, Y., Cheng, X., Gao, Z., Wang, Q., & Yang, H. (2024). Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks. arXiv preprint arXiv:2405.11704.

Saad, E. W., Prokhorov, D. V., & Wunsch, D. C. (1998). Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on neural networks, 9(6), 1456-1470.

Schmidt, R. M. (2019). Recurrent neural networks (rnns): A gentle introduction and overview. arXiv preprint arXiv:1912.05911.

Yao, J., Wu, T., & Zhang, X. (2023). Improving depth gradient continuity in transformers: A comparative study on monocular depth estimation with cnn. arXiv preprint arXiv:2308.08333.

Yan, X., Wang, W., Xiao, M., Li, Y., & Gao, M. (2024). Survival Prediction Across Diverse Cancer Types Using Neural Networks. arXiv preprint arXiv:2404.08713.

Liu, Z., Xia, X., Zhang, H., & Xie, Z. (2021, May). Analyze the impact of the epidemic on New York taxis by machine learning algorithms and recommendations for optimal prediction algorithms. In Proceedings of the 2021 3rd International Conference on Robotics Systems and Automation Engineering (pp. 46-52).

Hong, S., He, Y., Zhang, J., Jiang, C., & Deng, Y. (2022, April). Remaining Useful Life Prediction via Bayesian Temporal Convolutional Networks. In 2021 International Conference on Advanced Computing and Endogenous Security (pp. 1-8). IEEE.

Liu, H., Li, I., Liang, Y., Sun, D., Yang, Y., & Yang, H. (2024). Research on deep learning model of feature extraction based on convolutional neural network. arXiv preprint arXiv:2406.08837.