Exploring Anomaly Detection and Risk Assessment in Financial Markets Using Deep Neural Networks
Keywords:
Deep Learning; GRU, Attention Mechanism, CA Module; Anomaly DetectionAbstract
In this paper, deep learning technology, along with a Gated Recurrent Unit (GRU) combined with an attention mechanism, is used to enhance the recognition ability and risk assessment accuracy of abnormal trading behavior in financial markets. The GRU effectively solves the problem of gradient vanishing in traditional recurrent neural networks through its unique gated structure, allowing the model to learn more stable and effective feature representations in long sequence data. On this basis, the contextual attention (CA) module in the attention mechanism is introduced, enabling the model to automatically learn and assign different weights to various parts of the input sequence. Combined with bidirectional GRU and the attention mechanism, the model can not only capture temporal dependencies in the sequence but also highlight the key features that affect market anomalies, thus improving the model's ability to understand complex market dynamics.
References
E. M. Knorr and R. T. Ng, "Algorithms for Mining Distance-Based Outliers in Large Data-Sets," in Proceedings of the 24th International Conference on Very Large Databases, New York, Aug. 1998, pp. 392-403. Available from: https://www.vldb.org/conf/1998/p392.pdf
S. Ramaswamy, R. Rastogi, and K. Shim, "Efficient algorithms of mining outliers from large datasets," in Proceedings of the ACM SIGMOD International Conference on Management of Data, Dallas, USA, 2000, vol. 29, no. 2, pp. 427-438. Available from: https://doi.org/10.1145/342009.335437
X. Yan, W. Wang, M. Xiao, Y. Li, and M. Gao, "Survival prediction across diverse cancer types using neural networks," in Proceedings of the 2024 7th International Conference on Machine Vision and Applications, Mar. 2024, pp. 134-138. Available from: https://doi.org/10.1145/3653946.3653966
Z. Liu, X. Xia, H. Zhang, and Z. Xie, "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, May 2021, pp. 46-52. Available from: https://doi.org/10.1145/3475851.3475861
S. M. Erfani, S. Rajasegarar, S. Karunasekera, et al., "High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning," Pattern Recognition, vol. 58, pp. 121-134, 2016. Available from: https://doi.org/10.1016/j.patcog.2016.03.028
C. W. Chuah, W. He, and D.-S. Huang, "GMean—a semi-supervised GRU and K-mean model for predicting the TF binding site," Scientific Reports, vol. 14, no. 1, p. 2539, 2024.
Available from: https://doi.org/10.1038/s41598-024-52933-4
J. Yu and Y. Zhu, "A Data-Driven Approach to Predict Default Risk of Loan for Online Peer-to-Peer (P2P) Lending," in Proceedings of the Fifth International Conference on Communication Systems & Network Technologies, IEEE, 2015, pp. 609-613. Available from: https://doi.org/10.1109/CSNT.2015.25
R. Emekter, Y. Tu, B. Jirasakuldech, et al., "Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending," Applied Economics, vol. 47, no. 1, pp. 54-70, 2015. Available from: https://doi.org/10.1080/00036846.2014.962222
V. Aksakalli, Malekipirbazari, et al., "Risk Assessment in Social Lending via Random Forests," Expert Systems with Applications, vol. 42, no. 10, pp. 4621-4631, 2015. Available from: https://doi.org/10.1016/j.eswa.2015.02.001
A. Byanjankar, M. Heikkilä, and J. Mezei, "Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach," in Proceedings of the IEEE Symposium on Computational Intelligence, IEEE, 2015, pp. 719-725. Available from: https://doi.org/10.1109/SSCI.2015.109
R. Cahuantzi, X. Chen, and S. Güttel, "A comparison of LSTM and GRU networks for learning symbolic sequences," in Science and Information Conference, Cham: Springer Nature Switzerland, 2023. Available from: https://doi.org/10.1007/978-3-031-37963-5_53
B. Tu, et al., "Real-time prediction of ROP based on GRU-Informer," Scientific Reports, vol. 14, no. 1, p. 2133, 2024.
Available from: https://doi.org/10.1038/s41598-024-52261-7
Z. Liu, M. Wu, B. Peng, Y. Liu, Q. Peng, and C. Zou, "Calibration Learning for Few-shot Novel Product Description," in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2023, pp. 1864-1868. Available from: https://doi.org/10.1145/3539618.3591959
K. Xu, Y. Wu, Z. Li, R. Zhang, and Z. Feng, "Investigating Financial Risk Behavior Prediction Using Deep Learning and Big Data," International Journal of Innovative Research in Engineering and Management, vol. 11, no. 3, pp. 77-81, 2024. Available from: https://doi.org/10.55524/ijirem.2024.11.3.12
M. Sun, Z. Feng, Z. Li, W. Gu, and X. Gu, "Enhancing Financial Risk Management through LSTM and Extreme Value Theory: A High-Frequency Trading Volume Approach," Journal of Computer Technology and Software, vol. 3, no. 3, 2024. Available from: https://doi.org/10.5281/zenodo.12669410
D. Sun, Y. Liang, Y. Yang, Y. Ma, Q. Zhan, and E. Gao, "Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learning," arXiv preprint arXiv:2406.08838, 2024. Available from: https://doi.org/10.48550/arXiv.2406.08838
J. Wang, H. Zhang, Y. Zhong, Y. Liang, R. Ji, and Y. Cang, "Advanced Multimodal Deep Learning Architecture for Image-Text Matching," arXiv preprint arXiv:2406.15306, 2024. Available from: https://doi.org/10.1109/ICETCI61221.2024.10594167
H. Yang, Y. Zi, H. Qin, H. Zheng, and Y. Hu, "Advancing Emotional Analysis with Large Language Models," Journal of Computer Science and Software Applications, vol. 4, no. 3, pp. 8-15, 2024. Available from: https://doi.org/10.5281/zenodo.12204513
