Research on Financial Multi-Asset Portfolio Risk Prediction Model Based on Convolutional Neural Networks and Image Processing
Keywords:
Multi-Asset Portfolio; Risk Management; Convolutional Neural Network; Image Processing; Financial Data Visualization; Risk PredictionAbstract
In today's complex and volatile financial market environment, risk management of multi-asset portfolios faces significant challenges. Traditional risk assessment methods, due to their limited ability to capture complex correlations between assets, find it difficult to effectively cope with dynamic market changes. This paper proposes a multi-asset portfolio risk prediction model based on Convolutional Neural Networks (CNN). By utilizing image processing techniques, financial time series data are converted into two-dimensional images to extract high-order features and enhance the accuracy of risk prediction. Through empirical analysis of data from multiple asset classes such as stocks, bonds, commodities, and foreign exchange, the results show that the proposed CNN model significantly outperforms traditional models in terms of prediction accuracy and robustness, especially under extreme market conditions. This research provides a new method for financial risk management, with important theoretical significance and practical value.
References
Z. Zhao, M. Liu, and X. Zhang, "Investment portfolio selection and heterogeneous asset pricing research of multiple risk assets," Economic Dynamics, vol. 2023, no. 04, pp. 59-78, 2023.
H. Wu, "Multi-focus image fusion based on deep transform convolutional neural network," M.S. thesis, Chongqing Univ. of Posts and Telecommunications, Chongqing, China, 2022. Available from: https://doi.org/10.1016/j.inffus.2016.12.001
Y. Wang, "Research on image super-resolution algorithm based on image prior and convolutional neural network," M.S. thesis, Shaanxi Univ. of Science and Technology, Xi'an, China, 2023. Available from: https://doi.org/10.1109/AIID51893.2021.9456580
Q. Hu, "Image blind deblurring algorithm based on multi-scale convolutional neural network," M.S. thesis, Xi'an Univ. of Architecture and Technology, Xi'an, China, 2022. Available from: http://dx.doi.org/10.1109/ACCESS.2023.3245150
M. Zeng, "Ceramic image recognition and its application based on convolutional neural network," M.S. thesis, Jingdezhen Ceramic Univ., Jingdezhen, China, 2021. Available from: https://doi.org/10.1109/INDISCON58499.2023.10270728
M. Zhu, "Remote sensing image fusion based on multi-modal convolutional neural network," M.S. thesis, Yantai Univ., Yantai, China, 2022. Available from: http://dx.doi.org/10.1109/JSTARS.2018.2805923
Y. Tang, "Research on image denoising method based on multi-scale convolutional neural network," Wireless Internet Technology, vol. 19, no. 24, pp. 154-156, 2022. Available from: https://doi.org/10.1109/ELECO60389.2023.10416038
X. Liu, L. Wang, Q. Zhang, J. Wang, and X. Li, "Multi-spectral palmprint recognition technology based on convolutional neural network," Journal of Zhengzhou University (Science Edition), vol. 53, no. 03, pp. 50-55, 2021. Available from: https://doi.org/10.15439/2019F248
Y. Wu, "Research on lightweight image classification technology based on convolutional neural network," M.S. thesis, Yanshan Univ., Qinhuangdao, China, 2023. Available from: http://dx.doi.org/10.1109/ICBAIE52039.2021.9389911
X. Wang, "Research on fusion algorithm of hyperspectral and multispectral images based on convolutional neural network," M.S. thesis, Hefei Univ. of Technology, Hefei, China, 2021. Available from: https://doi.org/10.1016/j.sigpro.2023.109058
W. Ouyang, "Research on multi-modal image fusion algorithm based on convolutional neural network," M.S. thesis, Wuhan Univ. of Science and Technology, Wuhan, China, 2023. Available from: https://doi.org/10.1109/ICISC44355.2019.9036373
Y. Liu, Z. Zhang, X. Xia, and X. Han, "Application of image recognition technology based on convolutional neural network in breeding work," Modern Agricultural Equipment, vol. 44, no. 03, pp. 57-60, 2023. Available from: http://dx.doi.org/10.62051/ijcsit.v4n1.28
Q. Wang, L. Li, J. Gui, and X. Mao, "Defect detection and classification of lithium battery electrode sheets based on image processing and convolutional neural network," Manufacturing Automation, vol. 45, no. 10, pp. 50-54, 2023. Available from: https://link.springer.com/article/10.1007/s10845-019-01484-x
X. Liu, R. Zhou, and W. Guo, "Quantum linear convolution and its application in image processing," Acta Automatica Sinica, vol. 48, no. 06, pp. 1504-1519, 2022. Available form: https://doi.org/10.16383/j.aas.c210637
L. Wu, J. Xia, Y. Zhu, C. Chen, K. Qiao, F. Cao, and J. Pan, "Application progress of image processing technology based on convolutional neural network in blueberry planting," Shanghai Agricultural Science and Technology, no. 05, pp. 31-34, 2023. Available from: https://doi.org/10.3389/fpls.2022.868745
W. Yu, "Implementation of license plate image recognition technology based on convolutional neural network," Information Recording Materials, vol. 23, no. 05, pp. 154-156, 2022. Available from: https://doi.org/10.1145/3177404.3177436
M. B. Gordy and S. Juneja, “Nested Simulation in Portfolio Risk Measurement,” Management Science, vol. 56, no. 10, pp. 1833–1848, Oct. 2010, Available from: https://doi.org/10.1287/mnsc.1100.1213
Y. Mo, et al., "Large language model (LLM) AI text generation detection based on transformer deep learning algorithm," Int. Journal of Engineering and Management Research, vol. 14, no. 2, pp. 154-159, 2024. Available from: https://doi.org/10.48550/arXiv.2405.06652
H. Gong and M. Wang, "A duality approach for regret minimization in average-award ergodic Markov decision processes," in Learning for Dynamics and Control, PMLR, 2020, pp. 862-883. Available from: https://proceedings.mlr.press/v120/gong20a.html
K. Li, J. Wang, X. Wu, X. Peng, R. Chang, X. Deng, et al., "Optimizing automated picking systems in warehouse robots using machine learning," arXiv preprint, arXiv:2408.16633, 2024. Available from: http://dx.doi.org/10.48550/arXiv.2408.16633
L. Wang, Y. Cheng, H. Gong, J. Hu, X. Tang, and I. Li, "Research on dynamic data flow anomaly detection based on machine learning," arXiv preprint, arXiv:2409.14796, 2024. Available from: https://doi.org/10.48550/arXiv.2409.14796
Y. S. Kim, R. Giacometti, S. T. Rachev, F. J. Fabozzi, and D. Mignacca, “Measuring financial risk and portfolio optimization with a non-Gaussian multivariate model,” Annals of Operations Research, vol. 201, no. 1, pp. 325–343, Nov. 2012, Available from: https://doi.org/10.1007/s10479-012-1229-8
K. Sutiene et al., “Enhancing portfolio management using artificial intelligence: literature review,” Frontiers in artificial intelligence, vol. 7, Apr. 2024, Available from: https://doi.org/10.3389/frai.2024.1371502
Y. Qiao, K. Li, J. Lin, R. Wei, C. Jiang, Y. Luo, and H. Yang, "Robust domain generalization for multi-modal object recognition," in Proc. 2024 5th Int. Conf. on Artificial Intelligence and Electromechanical Automation (AIEA), 2024, pp. 392-397. Available from: https://doi.org/10.48550/arXiv.2408.05831
D. Bhatt et al., “CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope,” Electronics, vol. 10, no. 20, p. 2470, Oct. 2021, Available from: https://doi.org/10.3390/electronics10202470
Z. Li, et al., "Stock market analysis and prediction using LSTM: A case study on technology stocks," Innovations in Applied Engineering and Technology, pp. 1-6, 2023. Available from: http://dx.doi.org/10.62836/iaet.v2i1.162
“Portfolio Risk Analysis,” Google Books, 2024. https://books.google.com/books?hl=en&lr=&id=7y48w5XUlAYC&oi=fnd&pg=PP1&dq=portfolio+risk&ots=RWbAq1edrr&sig=tx7JtxBDLQHJBI-B794Hd-KURG8#v=onepage&q=portfolio%20risk&f=false (accessed Dec. 15, 2024). Available from: https://www.fe.training/free-resources/portfolio-management/portfolio-risk-management/
K. Jacques and P. Nigro, “Risk-based capital, portfolio risk, and bank capital: A simultaneous equations approach,” Journal of Economics and Business, vol. 49, no. 6, pp. 533–547, Nov. 1997, Available from: https://doi.org/10.1016/s0148-6195(97)00038-6
J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol. 61, no. 61, pp. 85–117, Jan. 2015, Available from: https://doi.org/10.1016/j.neunet.2014.09.003.
C. Qian, et al., "WeatherDG: LLM-assisted procedural weather generation for domain-generalized semantic segmentation," arXiv preprint, arXiv:2410.12075, 2024. Available from: http://dx.doi.org/10.48550/arXiv.2410.12075
Hamidreza Haddadian, Morteza Baky Haskuee, and Gholamreza Zomorodian, “A Hybrid Artificial Intelligence Approach to Portfolio Management,” Iranian journal of finance, vol. 6, no. 1, pp. 1–27, Jan. 2022, Available from: https://doi.org/10.30699/ijf.2021.287131.1237.
K. Li, J. Chen, D. Yu, D. Tang, X. Qiu, J. Li, et al., "Deep reinforcement learning-based obstacle avoidance for robot movement in warehouse environments," arXiv preprint, arXiv:2409.14972, 2024. Available from: https://doi.org/10.48550/arXiv.2409.14972
L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” Journal of Big Data, vol. 8, no. 1, Mar. 2021, Available from: https://doi.org/10.1186/s40537-021-00444-8.
K. Li, X. P. Song, J. Wang, and B. Hong, "The application of augmented reality (AR) in remote work and education," arXiv preprint, arXiv:2404.10579, 2024. Available from: https://doi.org/10.48550/arXiv.2404.10579
C. Jin, T. Che, H. Peng, Y. Li, D. N. Metaxas, and M. Pavone, "Learning from teaching regularization: Generalizable correlations should be easy to imitate," arXiv preprint, arXiv:2402.02769, 2024. Available from: https://doi.org/10.48550/arXiv.2402.02769
K. Li, X. P. Wang, J. Song, B. Hong, and J. Wang, "Utilizing deep learning to optimize software development processes," Journal of Computer Technology and Applied Mathematics, vol. 1, no. 1, pp. 70-76, 2024. Available from: https://doi.org/10.5281/zenodo.11004006
S. S. Sumit, J. Watada, A. Roy, and D. Rambli, “In object detection deep learning methods, YOLO shows supremum to Mask R-CNN,” Journal of Physics: Conference Series, vol. 1529, p. 042086, Apr. 2020, Available from: https://doi.org/10.1088/1742-6596/1529/4/042086.
N. Sang, W. Cai, C. Yu, M. Sui, and H. Gong, "Enhanced investment prediction via advanced deep learning ensemble," Preprints, 2024, Available from: http://dx.doi.org/10.20944/preprints202409.2029.v1
H. Peng, X. Xie, K. Shivdikar, M. A. Hasan, J. Zhao, S. Huang, et al., "Maxk-GNN: Extremely fast GPU kernel design for accelerating graph neural networks training," in Proc. 29th ACM Int. Conf. on Architectural Support for Programming Languages and Operating Systems, vol. 2, 2024, pp. 683-698. Available from: https://arxiv.org/pdf/2312.08656
B. P. Bhuyan and T. P. Singh, “Artificial Intelligence in Financial Portfolio Management,” www.igi-global.com, 2022. Available from: https://www.igi-global.com/chapter/artificial-intelligence-in-financial-portfolio-management/311188
A. Gunjan and S. Bhattacharyya, “A brief review of portfolio optimization techniques,” Artificial Intelligence Review, Sep. 2022, Available from: https://doi.org/10.1007/s10462-022-10273-7.
C. Qian, et al., "WeatherDG: LLM-assisted procedural weather generation for domain-generalized semantic segmentation," arXiv preprint, arXiv:2410.12075, 2024. Available from: https://doi.org/10.48550/arXiv.2410.12075
Z. Wu, H. Gong, J. Chen, Y. Zuru, L. Tan, and G. Shi, "A lightweight GAN-based image fusion algorithm for visible and infrared images," arXiv preprint, arXiv:2409.15332, 2024. Available from: http://dx.doi.org/10.48550/arXiv.2409.15332
Y. Sun, Y. Duan, H. Gong, and M. Wang, "Learning low-dimensional state embeddings and metastable clusters from time series data," Advances in Neural Information Processing Systems, vol. 32, 2019. Available from: http://dx.doi.org/10.48550/arXiv.2002.05909
P. Bharati and A. Pramanik, “Deep Learning Techniques—R-CNN to Mask R-CNN: A Survey,” Computational Intelligence in Pattern Recognition, vol. 999, pp. 657–668, Aug. 2019, Available from: https://doi.org/10.1007/978-981-13-9042-5_56.
T. Zhou, J. Zhao, Y. Luo, X. Xie, W. Wen, C. Ding, and X. Xu, "Adapi: Facilitating DNN model adaptivity for efficient private inference in edge computing," arXiv preprint, Available from: https://doi.org/10.48550/arXiv.2407.05633 arXiv:2407.05633, 2024.
