Machine Learning-Based Intelligent Risk Management and Arbitrage System for Fixed Income Markets: Integrating High-Frequency Trading Data and Natural Language Processing
DOI:
https://doi.org/10.5281/zenodo.13858262ARK:
https://n2t.net/ark:/40704/JIEAS.v2n5a09References:
40Keywords:
Machine Learning, Fixed Income Markets, Risk Management, Arbitrage DetectionAbstract
This paper introduces risk management and competitive advantage in fixed-income trading, machine learning, high-volume trading (HFT), and natural language processing (NLP). The system integrates advanced analytics and deep learning techniques to improve decision-making, providing real-time risk assessment and detection arbitrage. The main innovations include combining HFT data, which captures the random product of microstructure dynamics, and NLP, which removes the agreement from the non-disordered text, such as financial information and management information. The system employs a hierarchical model, using gradient-boosting machines and neural networks to capture complex temporal dependencies. Results from rigorous testing and real-time performance evaluations show significant improvements in forecasting accuracy, risk management, and correlation analysis. Different compared to traditional methods. The system's adaptability in various business conditions underscores its ability to improve business stability and liquidity. In addition, ethical decision-making and management are addressed through AI-declared processes, making decision-making more transparent. This research demonstrates the transformative potential of integrating AI technology in the fixed-income industry, supporting better and more informed business strategies.
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