Advancing Financial Forecasts: A Deep Dive into Memory Attention and Long-Distance Loss in Stock Price Predictions
Source abstract
Abstract
In the context of the rapid evolution of financial markets, the precise prediction of stock prices has become increasingly complex and challenging, influenced by a myriad of factors including macroeconomic indicators, company financial conditions, and market sentiment. A model integrating modern machine learning techniques has been introduced in this study, aimed at enhancing the accuracy of stock price prediction. To more effectively capture long-term dependencies in time series data, a novel memory attention module has been innovatively integrated and a unique long-distance loss function has been designed. Through a series of experimental validations, the effectiveness and superiority of this model in the realm of stock price prediction have been demonstrated, especially evident in the R2 evaluation metric, where an impressive score of 0.97 has been achieved. Furthermore, the purpose, methodology, data sources, and key results of this research have been elaborately detailed, aiming to provide fresh perspectives and tools for the field of stock price prediction and lay a solid foundation for future related studies. Overall, this research has not only enhanced the accuracy of stock price prediction but also made innovative contributions in terms of methodology and practical applications, bringing new thoughts and possibilities to the domain of financial analysis and prediction.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Stock Market Forecasting Methods · Forecasting Techniques and Applications · Energy Load and Power Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
China Agricultural University, Beijing 100083, China
School of Business and Management, Jilin University, Jilin 130015, China
School of Government, Nanjing University, Nanjing 210008, China
Funding
National Natural Science Foundation of China
References (31)
Show source-provided references
1. Fuzzy time-series prediction model based on text features and network features10.1007/s00521-021-05834-w ↗
2. Prediction based mean-value-at-risk portfolio optimization using machine learning regression algorithms for multi-national stock markets10.1016/j.engappai.2023.105843 ↗
3. Zhang, Y., Liu, X., Wa, S., Liu, Y., Kang, J., and Lv, C. (2021). GenU-Net++: An Automatic Intracranial Brain Tumors Segmentation Algorithm on 3D Image Series with High Performance. Symmetry, 13.10.3390/sym13122395 ↗
4. Lin, X., Wa, S., Zhang, Y., and Ma, Q. (2022). A dilated segmentation network with the morphological correction method in farming area image Series. Remote. Sens., 14.10.3390/rs14081771 ↗
5. Li, Q., Ren, J., Zhang, Y., Song, C., Liao, Y., and Zhang, Y. (2023, January 9–13). Privacy-Preserving DNN Training with Prefetched Meta-Keys on Heterogeneous Neural Network Accelerators. Proceedings of the 2023 60th ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA.10.1109/DAC56929.2023.10247964 ↗
6. Zhang, Y., He, S., Wa, S., Zong, Z., Lin, J., Fan, D., Fu, J., and Lv, C. (2022). Symmetry GAN Detection Network: An Automatic One-Stage High-Accuracy Detection Network for Various Types of Lesions on CT Images. Symmetry, 14.10.3390/sym14020234 ↗
7. A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis10.7717/peerj-cs.1158 ↗
8. Forecasting movements of stock time series based on hidden state guided deep learning approach10.1016/j.ipm.2023.103328 ↗
9. Prediction of the Stock Market From Linguistic Phrases: A Deep Neural Network Approach10.4018/JDM.322020 ↗
10. Stock values predictions using deep learning based hybrid models10.1049/cit2.12052 ↗
11. Instance-based deep transfer learning with attention for stock movement prediction10.1007/s10489-022-03755-2 ↗
12. Modal decomposition-based hybrid model for stock index prediction10.1016/j.eswa.2022.117252 ↗
13. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30–37.
14. Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv.
15. Transformer-Gated Recurrent Unit Method for Predicting Stock Price Based on News Sentiments and Technical Indicators10.1109/ACCESS.2023.3298445 ↗
16. Li, C., and Qian, G. (2023). Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network. Appl. Sci., 13.10.3390/app13010222 ↗
17. Stock market index prediction using deep Transformer model10.1016/j.eswa.2022.118128 ↗
18. Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T.H., and Veloso, M. (2023). Financial Time Series Forecasting using CNN and Transformer. arXiv.
19. Xu, C., Li, J., Feng, B., and Lu, B. (2023). A Financial Time-Series Prediction Model Based on Multiplex Attention and Linear Transformer Structure. Appl. Sci., 13.10.3390/app13085175 ↗
20. Hu, J., Shen, L., and Sun, G. (2018, January 18–23). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00745 ↗
21. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8–14). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
22. Huang, Y., Liu, J., and Lv, C. (2023). Chains-BERT: A High-Performance Semi-Supervised and Contrastive Learning-Based Automatic Question-and-Answering Model for Agricultural Scenarios. Appl. Sci., 13.10.3390/app13052924 ↗
23. yfinance (2023, November 02). PYPI. Available online: https://pypi.org/project/yfinance/.
24. Sadorsky, P. (2021). A random forests approach to predicting clean energy stock prices. J. Risk Financ. Manag., 14.10.3390/jrfm14020048 ↗
25. Stock price forecast based on combined model of ARI-MA-LS-SVM10.1007/s00521-019-04698-5 ↗
26. Zhang, L., Wang, R., Li, Z., Li, J., Ge, Y., Wa, S., Huang, S., and Lv, C. (2023). Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention. Information, 14.10.3390/info14090500 ↗
27. Lohit, S., Wang, Q., and Turaga, P. (2019, January 15–20). Temporal transformer networks: Joint learning of invariant and discriminative time warping. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.01271 ↗
28. Lu, Y., Rai, H., Chang, J., Knyazev, B., Yu, G., Shekhar, S., Taylor, G.W., and Volkovs, M. (2021, January 11–17). Context-aware scene graph generation with seq2seq transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada.10.1109/ICCV48922.2021.01563 ↗
29. Zhou, J., Li, X., Ding, T., You, C., Qu, Q., and Zhu, Z. (2022, January 17–23). On the optimization landscape of neural collapse under mse loss: Global optimality with unconstrained features. Proceedings of the International Conference on Machine Learning. PMLR, Baltimore, ML, USA.
30. Meyer, G.P. (2021, January 20–25). An alternative probabilistic interpretation of the huber loss. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR46437.2021.00522 ↗
31. Saleh, R.A., and Saleh, A. (2022). Statistical properties of the log-cosh loss function used in machine learning. arXiv.