[Paper Review] Design and Optimization of Big Data and Machine Learning-Based Risk Monitoring System in Financial Markets

Liyang Wang, Yu Cheng+2 more|arXiv (Cornell University)|Jul 28, 2024

Big Data Technologies and Applications4 citations

TL;DR

This paper proposes a four-layer big data and machine learning-based risk monitoring system for financial markets, integrating LSTM, Random Forest, Gradient Boosting Trees, and Apache Flink for real-time risk detection. The system significantly improves accuracy and efficiency in identifying market crash risks, outperforming traditional methods in dynamic, high-volume environments.

ABSTRACT

With the increasing complexity of financial markets and rapid growth in data volume, traditional risk monitoring methods no longer suffice for modern financial institutions. This paper designs and optimizes a risk monitoring system based on big data and machine learning. By constructing a four-layer architecture, it effectively integrates large-scale financial data and advanced machine learning algorithms. Key technologies employed in the system include Long Short-Term Memory (LSTM) networks, Random Forest, Gradient Boosting Trees, and real-time data processing platform Apache Flink, ensuring the real-time and accurate nature of risk monitoring. Research findings demonstrate that the system significantly enhances efficiency and accuracy in risk management, particularly excelling in identifying and warning against market crash risks.

Motivation & Objective

• To address the limitations of traditional risk monitoring systems in handling the growing complexity and volume of financial data.
• To design a scalable, real-time risk monitoring architecture capable of processing large-scale financial data streams.
• To integrate advanced machine learning models with real-time stream processing for improved risk prediction accuracy.
• To optimize the system for early detection of systemic risk, particularly market crash events.

Proposed method

• The system employs a four-layer architecture integrating data ingestion, storage, processing, and analytics layers for end-to-end risk monitoring.
• Real-time data processing is enabled via Apache Flink, ensuring low-latency event handling and stream processing of financial data.
• Long Short-Term Memory (LSTM) networks are used to model temporal dependencies in financial time series for risk forecasting.
• Ensemble models including Random Forest and Gradient Boosting Trees are applied to enhance predictive accuracy and robustness.
• Feature engineering is performed on multi-source financial data, including market prices, trading volumes, and news sentiment.
• The system is optimized through hyperparameter tuning and model validation on historical crash events to improve detection performance.

Experimental results

Research questions

• RQ1 How can a scalable, real-time risk monitoring system be architected to handle high-velocity financial data streams?
• RQ2 To what extent do LSTM and ensemble models improve early detection of market crash risks compared to traditional methods?
• RQ3 What role does real-time stream processing play in enhancing the responsiveness and accuracy of risk alerts?
• RQ4 How does the integration of diverse data sources impact the system’s predictive performance?
• RQ5 What optimization strategies maximize the detection accuracy of critical risk events in financial markets?

Key findings

• The system demonstrates a significant improvement in risk detection accuracy, particularly in identifying precursors to market crashes.
• LSTM models effectively capture long-term dependencies in financial time series, enhancing prediction reliability.
• Ensemble models such as Gradient Boosting Trees and Random Forest reduce prediction variance and improve robustness.
• Apache Flink enables sub-second latency processing, ensuring real-time risk monitoring capabilities.
• The integrated system outperforms conventional risk monitoring frameworks in both speed and detection precision.
• The system achieves high F1-scores in detecting critical risk events during backtested historical market stress periods.