Abstract
Predicting future trends in financial time series data is a challenging task due to issues such as missing data, noise influence, and the heterogeneity gap between different data modalities. In this study, we propose a Global-Local Multimodal Guidance Fusion for Intelligent Stock Forecasting (GLMG) model to address these challenges. The GLMG model incorporates several novel components, including a wavelet transform-based approach for multi-scale feature extraction from image modality data, a Residual Modality Completion Module for handling missing data, a Global-Local Fusion Unit for balanced integration of cross-modal information, and a Stepwise Fusion module for reducing computational complexity and improving interpretability. We conduct extensive experiments on real-world stock market datasets to evaluate the performance of the GLMG model. The results demonstrate that our proposed approach outperforms methods, achieving an accuracy of 64.23% for stock risk early warning and 65.42% for stock movement forecasting, surpassing the best baseline by 6.30% and 6.28% respectively. The GLMG model also exhibits superior profitability, yielding a 16.23% profit while maintaining a low maximum drawdown of 7.96% in simulated trading scenarios.
J. Qin, B. You and F. Liu—Contributing equally to this work.
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References
Ahelegbey, D.F., Billio, M., Casarin, R.: Bayesian graphical models for structural vector autoregressive processes. J. Appl. Economet. 31(2), 357–386 (2016)
Ahelegbey, D.F., Cerchiello, P., Scaramozzino, R.: Network based evidence of the financial impact of covid-19 pandemic. Int. Rev. Financial Anal. 81, 102101 (2022). https://doi.org/10.1016/j.irfa.2022.102101. https://www.sciencedirect.com/science/article/pii/S1057521922000710
Buche, A., Chandak, M.B.: Enhancing predictive modeling for Indian banking stock trends: a fusion of BERT and attention-based BiLSTM approach. J. Intell. Fuzzy Syst. (2023). https://api.semanticscholar.org/CorpusID:261654967
Chen, Y., Wei, Z., Huang, X.: Incorporating corporation relationship via graph convolutional neural networks for stock price prediction. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, pp. 1655–1658. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3269206.3269269
Dai, W., An, Y., Long, W.: Price change prediction of ultra high frequency financial data based on temporal convolutional network. Procedia Comput. Sci. 199, 1177–1183 (2022)
Fataliyev, K., Liu, W.: MCASP: multi-modal cross attention network for stock market prediction. In: Muresan, S., et al. (eds.) Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association, Melbourne, Australia, pp. 67–77. Association for Computational Linguistics, November 2023. https://aclanthology.org/2023.alta-1.7
Feng, F., Chen, H., He, X., Ding, J., Sun, M., Chua, T.S.: Enhancing stock movement prediction with adversarial training (2019)
Gupta, U., Bhattacharjee, V., Bishnu, P.S.: StockNet—GRU based stock index prediction. Expert Syst. Appl. 207, 117986 (2022)
Huan, R., Zhong, G., Chen, P., Liang, R.: UniMF: a unified multimodal framework for multimodal sentiment analysis in missing modalities and unaligned multimodal sequences. IEEE Trans. Multimed., 1–16 (2023). https://doi.org/10.1109/TMM.2023.3338769
Jothimani, D., Shankar, R., Yadav, S.S.: Discrete wavelet transform-based prediction of stock index: a study on national stock exchange fifty index (2016)
Liu, M., et al.: SCINet: time series modeling and forecasting with sample convolution and interaction. Adv. Neural. Inf. Process. Syst. 35, 5816–5828 (2022)
Mandal, R., et al.: Enhancing stock price prediction with deep cross-modal information fusion network. Fluct. Noise Lett. (2023). https://doi.org/10.1142/S0219477524400170
Qin, J., Zong, L.: TS-BERT: a fusion model for pre-training time series-text representations (2021). https://openreview.net/forum?id=Fia60I79-4B
Zhang, Q., Zhang, Y., Bao, F., Zhang, C., Liu, P.: SMPDF: stock movement prediction based on stock prices and text. Int. J. Gener. Syst., 1–18 (2023). https://doi.org/10.1080/03081079.2023.2294132
Sun, L., Lian, Z., Liu, B., Tao, J.: Efficient multimodal transformer with dual-level feature restoration for robust multimodal sentiment analysis. IEEE Trans. Affect. Comput. 15(1), 309–325 (2024). https://doi.org/10.1109/TAFFC.2023.3274829
Thakkar, A., Chaudhari, K.: Information fusion-based genetic algorithm with long short-term memory for stock price and trend prediction. Appl. Soft Comput. 128, 109428 (2022). https://doi.org/10.1016/j.asoc.2022.109428. https://www.sciencedirect.com/science/article/pii/S1568494622005555
Wang, S., Qin, J., Rudolph, C., Nepal, S., Grobler, M.: R-Net: robustness enhanced financial time-series prediction with differential privacy. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1–9 (2022). https://api.semanticscholar.org/CorpusID:252625275
Yuan, Z., Li, W., Xu, H., Yu, W.: Transformer-based feature reconstruction network for robust multimodal sentiment analysis (2021)
Zeng, J., Zhou, J., Liu, T.: Mitigating inconsistencies in multimodal sentiment analysis under uncertain missing modalities. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 2924–2934 (2022)
Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)
Zhang, Q., Qin, C., Zhang, Y., Bao, F., Zhang, C., Liu, P.: Transformer-based attention network for stock movement prediction. Expert Syst. Appl. 202, 117239 (2022). https://doi.org/10.1016/j.eswa.2022.117239. https://www.sciencedirect.com/science/article/pii/S0957417422006170
Zuo, H., Liu, R., Zhao, J., Gao, G., Li, H.: Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities (2022)
Zuo, Y., Kita, E.: Stock price forecast using Bayesian network. Expert Syst. Appl. 39(8), 6729–6737 (2012)
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Qin, J., You, B., Liu, F. (2024). Intelligent Stock Forecasting by Iterative Global-Local Fusion. In: Huang, DS., Zhang, C., Guo, J. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14871. Springer, Singapore. https://doi.org/10.1007/978-981-97-5609-4_22
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