Volume- 11
Issue- 6
Year- 2024
DOI: 10.55524/ijirem.2024.11.6.5 | DOI URL: https://doi.org/10.55524/ijirem.2024.11.6.5
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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
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Keke Yu , Qi Shen, Qi Lou, Yitian Zhang, Xin Ni
This paper presents a new approach to improve revenue in the US bond market using deep learning (DRL) as an artificial intelligence-based market. This study addresses the persistent lack of capacity in this critical business by combining advanced machine learning techniques with the specialised knowledge of financial institutions in the city. A comprehensive multi-agent simulation environment is developed, incorporating key market microstructure features and risk management constraints. The DRL agent is trained using historical trading data from 2018 to 2022, sourced from the Municipal Securities Rulemaking Board's EMMA system. Experimental results demonstrate the agent's superior performance compared to benchmark strategies across various market conditions. The DRL agent consistently improves key liquidity metrics, including bid-ask spreads and market depth, while maintaining robust risk-adjusted returns. The study finds that the proposed approach enhances market efficiency and exhibits adaptability during periods of market stress. Potential impacts on municipal finances were discussed, including reducing the cost of borrowing for local governments and improving cost discovery. Although limitations such as activation capabilities and real-world challenges are recognised, research has yielded positive results for using AI in the financial industry. It is an excellent way to develop the urban economy in the future.
S. G. Ancy and J. Praveenchandar, "An Effective Machine Learning Algorithm for Forecasting the Market Value of a House," in 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), June 2024, pp. 1567–1574. IEEE. Available from: https://doi.org/10.1109/ICAAIC60222.2024.10575745
L. Hao, B. Wang, Z. Lu, and K. Hu, "Application of deep reinforcement learning in financial quantitative trading," in 2022 4th International Conference on Communications, Information System and Computer Engineering (CISCE), May 2022, pp. 466–471. IEEE. Available from: https://doi.org/10.1109/CISCE55963.2022.9851105
T. Sun, D. Huang, and J. Yu, "Market making strategy optimization via deep reinforcement learning," IEEE Access, vol. 10, pp. 9085–9093, 2022. Available from: https://doi.org/10.1109/ACCESS.2022.3143653
S. Chib, D. Tare, A. Samal, M. L. Mohan, G. V. Rajeshwari, and H. N. Prasanna, "Trend Tracking Artificial Intelligence Predictive Models for New and Highly Advanced Financial Markets," in 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE), May 2024, pp. 658–663. IEEE. Available from: https://doi.org/10.1109/IC3SE62002.2024.10593342
A. Kumar, S. Dodda, N. Kamuni, and R. K. Arora, "Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets," arXiv preprint arXiv:2404.07225, 2024. Available from: https://doi.org/10.48550/arXiv.2404.07225
Q. Shen, X. Wen, S. Xia, S. Zhou, and H. Zhang, "AI-Based Analysis and Prediction of Synergistic Development Trends in US Photovoltaic and Energy Storage Systems," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 5, pp. 36–46, 2024. Available from: https://doi.org/10.55524/ijircst.2024.12.5.6
H. Li, S. Zhou, B. Yuan, and M. Zhang, "Optimizing Intelligent Edge Computing Resource Scheduling Based on Federated Learning," Journal of Knowledge Learning and Science Technology, vol. 3, no. 3, pp. 235–260, 2024. Available from: https://doi.org/10.60087/jklst.vol3.n3.p.235-260
Y. Pu, Y. Zhu, H. Xu, Z. Wang, and M. Wei, "LSTM-Based Financial Statement Fraud Prediction Model for Listed Companies," Academic Journal of Sociology and Management, vol. 2, no. 5, pp. 20–31, 2024. Available from: https://doi.org/10.5281/zenodo.13762976
Y. Liu, H. Tan, G. Cao, and Y. Xu, "Enhancing User Engagement through Adaptive UI/UX Design: A Study on Personalized Mobile App Interfaces," 2024. Available from: https://doi.org/10.53469/wjimt.2024.07(05).01
D. Huang, M. Yang, X. Wen, S. Xia, and B. Yuan, "AI-Driven Drug Discovery: Accelerating the Development of Novel Therapeutics in Biopharmaceuticals," Journal of Knowledge Learning and Science Technology, vol. 3, no. 3, pp. 206–224, 2024. Available from: https://doi.org/10.60087/jklst.vol3.n3.p.206-224
H. Xu, S. Li, K. Niu, and G. Ping, "Utilizing Deep Learning to Detect Fraud in Financial Transactions and Tax Reporting," Journal of Economic Theory and Business Management, vol. 1, no. 4, pp. 61–71, 2024. Available from: https://doi.org/10.5281/zenodo.13294459
S. Wang, H. Zheng, X. Wen, and S. Fu, "Distributed High-Performance Computing Methods for Accelerating Deep Learning Training," Journal of Knowledge Learning and Science Technology, vol. 3, no. 3, pp. 108–126, 2024. Available from: https://doi.org/10.60087/jklst.v3.n3.p108-126
H. Lei, B. Wang, Z. Shui, P. Yang, and P. Liang, "Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology," arXiv preprint arXiv:2404.04492, 2024. Available from: https://doi.org/10.48550/arXiv.2404.04492
B. Wang, H. Zheng, K. Qian, X. Zhan, and J. Wang, "Edge computing and AI-driven intelligent traffic monitoring and optimization," Applied and Computational Engineering, vol. 77, pp. 225–230, 2024. Available from: https://doi.org/10.54254/27552721/77/2024MA0062
S. Wang, K. Xu, and Z. Ling, "Deep Learning-Based Chip Power Prediction and Optimization: An Intelligent EDA Approach," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 4, pp. 77–87, 2024. Available from: https://doi.org/10.55524/ijircst.2024.12.4.13
K. Xu, H. Zheng, X. Zhan, S. Zhou, and K. Niu, "Evaluation and Optimization of Intelligent Recommendation System Performance with Cloud Resource Automation Compatibility," 2024. Available from: https://www.preprints.org/manuscript/202407.2199
H. Zheng, K. Xu, H. Zhou, Y. Wang, and G. Su, "Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis," Academic Journal of Science and Technology, vol. 10, no. 1, pp. 62–68, 2024. Available from: https://doi.org/10.54097/v160aa61
B. Liu and Y. Zhang, "Implementation of seamless assistance with Google Assistant leveraging cloud computing," Journal of Cloud Computing, vol. 12, no. 4, pp. 1–15, 2023. Available from:http://dx.doi.org/10.54254/2755-2721/64/20241383
M. Zhang, B. Yuan, H. Li, and K. Xu, "LLM-Cloud Complete: Leveraging Cloud Computing for Efficient Large Language Model-based Code Completion," Journal of Artificial Intelligence General Science, vol. 5, no. 1, pp. 295–326, 2024. Available from: https://doi.org/10.60087/jaigs.v5i1.200
P. Li, Y. Hua, Q. Cao, and M. Zhang, "Improving the Restore Performance via Physical-Locality Middleware for Backup Systems," in Proceedings of the 21st International Middleware Conference, Dec. 2020, pp. 341–355. Available from: https://doi.org/10.1145/3423211.3425691
S. Zhou, B. Yuan, K. Xu, M. Zhang, and W. Zheng, "The Impact of Pricing Schemes on Cloud Computing and Distributed Systems," Journal of Knowledge Learning and Science Technology, vol. 3, no. 3, pp. 193–205, 2024. Available from: https://doi.org/10.60087/jklst.v3.n3.p206-224
F. Shang, F. Zhao, M. Zhang, J. Sun, and J. Shi, "Personalized Recommendation Systems Powered By Large Language Models: Integrating Semantic Understanding and User Preferences," International Journal of Innovative Research in Engineering and Management, vol. 11, no. 4, pp. 39–49, 2024. Available from: https://doi.org/10.55524/ijirem.2024.11.4.6
J. Sun, X. Wen, G. Ping, and M. Zhang, "Application of News Analysis Based on Large Language Models in Supply Chain Risk Prediction," Journal of Computer Technology and Applied Mathematics, vol. 1, no. 3, pp. 55–65, 2024. Available from:https://doi.org/10.5281/zenodo.13377298
F. Zhao, M. Zhang, S. Zhou, and Q. Lou, "Detection of Network Security Traffic Anomalies Based on Machine Learning KNN Method," Journal of Artificial Intelligence General Science, vol. 1, no. 1, pp. 209–218, 2024. Available from: https://doi.org/10.60087/jaigs.v1i1.213
S. Wang, H. Zheng, X. Wen, K. Xu, and H. Tan, "Enhancing chip design verification through AI-powered bug detection in RTL code," Applied and Computational Engineering, vol. 92, pp. 27–33, 2024. Available from: https://doi.org/10.60087/jaigs.v1i1.213
K. Yu, Q. Bao, H. Xu, G. Cao, and S. Xia, "An Extreme Learning Machine Stock Price Prediction Algorithm Based on the Optimization of the Crown Porcupine Optimization Algorithm with an Adaptive Bandwidth Kernel Function Density Estimation Algorithm," 2024. Available from: https://www.preprints.org/manuscript/202408.1116
X. Zhang, "Machine learning insights into digital payment behaviors and fraud prediction," Applied and Computational Engineering, vol. 67, pp. 61–67, 2024. Available from: https://www.preprints.org/manuscript/202407.1431
X. Zhang, "Analyzing Financial Market Trends in Cryptocurrency and Stock Prices Using CNN-LSTM Models," 2024. Available from: https://www.preprints.org/manuscript/202407.1119
H. Zhang, et al., "Enhancing facial micro-expression recognition in low-light conditions using attention-guided deep learning," Journal of Economic Theory and Business Management, vol. 1, no. 5, pp. 12–22, 2024. Available from: https://doi.org/10.5281/zenodo.13933725
J. Wang, T. Lu, L. Li, and D. Huang, "Enhancing personalized search with AI: A hybrid approach integrating deep learning and cloud computing," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 5, pp. 127–138, 2024. Available from: https://doi.org/10.55524/ijircst.2024.12.5.17
S. Zhou, W. Zheng, Y. Xu, and Y. Liu, "Enhancing user experience in VR environments through AI-driven adaptive UI design," Journal of Artificial Intelligence General Science, vol. 6, no. 1, pp. 59–82, 2024. Available from: https://doi.org/10.60087/jaigs.v6i1.230
M. Yang, D. Huang, H. Zhang, and W. Zheng, "AI-enabled precision medicine: Optimizing treatment strategies through genomic data analysis," Journal of Computer Technology and Applied Mathematics, vol. 1, no. 3, pp. 73–84, 2024. Available from: https://doi.org/10.5281/zenodo.13380619
X. Wen, Q. Shen, W. Zheng, and H. Zhang, "AI-driven solar energy generation and smart grid integration: A holistic approach to enhancing renewable energy efficiency," International Journal of Innovative Research in Engineering and Management, vol. 11, no. 4, pp. 55–66, 2024. Available from:
Y. Zhang, W. Bi, and R. Song, "Research on deep learning-based authentication methods for E-signature verification in financial documents," Academic Journal of Sociology and Management, vol. 2, no. 6, pp. 35–43, 2024. Available from:
Z. Zhou, S. Xia, M. Shu, and H. Zhou, "Fine-grained abnormality detection and natural language description of medical CT images using large language models," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 6, pp. 52–62, 2024. Available from: https://doi.org/10.5281/zenodo.14161744
Y. Zhang, Y. Liu, and S. Zheng, "A graph neural network-based approach for detecting fraudulent small-value high-frequency accounting transactions," Academic Journal of Sociology and Management, vol. 2, no. 6, pp. 25–34, 2024. Available from: https://doi.org/10.5281/zenodo.14161459
K. Yu, Q. Shen, Q. Lou, Y. Zhang, and X. Ni, "A deep reinforcement learning approach to enhancing liquidity in the US municipal bond market: An intelligent agent-based trading system," International Journal of Engineering and Management Research, vol. 14, no. 5, pp. 113–126, 2024. Available from: https://d197for5662m48.cloudfront.net/documents/publicationstatus/234981/preprint_pdf/fb7503a454c7a90b67931b85e194b423.pdf
Y. Wang, Y. Zhou, H. Ji, Z. He, and X. Shen, "Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data," in 2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE), IEEE, Mar. 2024, pp. 1425–1429. Available from: https://doi.org/10.1109/ICAACE61206.2024.10548953
T. Lu, M. Jin, M. Yang, and D. Huang, "Deep learning-based prediction of critical parameters in CHO cell culture process and its application in monoclonal antibody production," International Journal of Advance in Applied Science Research, vol. 3, pp. 108–123, 2024. Available from:
https://h-tsp.com/index.php/ijaasr/article/view/69
S. Xia, Y. Zhu, S. Zheng, T. Lu, and K. X., "A deep learning-based model for P2P microloan default risk prediction," International Journal of Innovative Research in Engineering and Management, vol. 11, no. 5, pp. 110–120, 2024. Available from: https://www.mdpi.com/1099-4300/24/6/801#
W. Zheng, M. Yang, D. Huang, and M. Jin, "A deep learning approach for optimizing monoclonal antibody production process parameters," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 6, pp. 18–29, 2024. Available from: https://doi.org/10.55524/ijircst.2024.12.6.4
H. Zheng, K. Xu, M. Zhang, H. Tan, and H. Li, "Efficient resource allocation in cloud computing environments using AI-driven predictive analytics," Applied and Computational Engineering, vol. 82, pp. 6–12, 2024. Available from: https://doi.org/10.54254/2755-2721/82/2024GLG0055
T. Lu, Z. Zhou, J. Wang, and Y. Wang, "A large language model-based approach for personalized search results re-ranking in professional domains," The International Journal of Language Studies, vol. 1, no. 2, pp. 1–6, 2024. Available from: https://doi.org/10.60087/ijls.v1.n2.001
X. Ni, L. Yan, K. X., and Y. Liu, "A hierarchical Bayesian market mix model with causal inference for personalized marketing optimization," Journal of Artificial Intelligence General Science, vol. 6, no. 1, pp. 378–396, 2024. Available from:https://doi.org/10.60087/jaigs.v6i1.261
H. Zhang, Y. Pu, S. Zheng, and L. Li, "AI-driven M&A target selection and synergy prediction: A machine learning-based approach," Social Science Journal for Advanced Research, vol. 4, no. 5, pp. 40–54, 2024. Available from: https://doi.org/10.60087/jaigs.v6i1.260
Y. Zhu, K. Yu, M. Wei, Y. Pu, and Z. Wang, "AI-enhanced administrative prosecutorial supervision in financial big data: New concepts and functions for the digital era," Social Science Journal for Advanced Research, vol. 4, no. 5, pp. 40–54, 2024. Available from: https://doi.org/10.5281/zenodo.13766965
S. Wang, Y. Zhu, Q. Lou, and M. Wei, "Utilizing artificial intelligence for financial risk monitoring in asset management," Academic Journal of Sociology and Management, vol. 2, no. 5, pp. 11–19, 2024. Available from: https://doi.org/10.5281/zenodo.13762069
Department of Economics, University of California, Santa Barbara, CA, USA
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