Early Warning Indicators for Financial Market Anomalies: A Multi-Signal Integration Approach

This study proposes a novel multi-signal integration approach for early detection of financial market anomalies through the systematic combination of diverse market indicators. Traditional anomaly detection methods often suffer from limited predictive capacity due to their reliance on isolated signal categories and inability to capture complex cross-market relationships. We address these limitations by developing a hierarchical integration framework that synthesizes market microstructure metrics, technical indicators, fundamental data, sentiment measures, and cross-asset signals into a unified detection system. The methodology employs a BiLSTM-attention architecture with optimized signal selection mechanisms to identify emerging anomalies across multiple temporal horizons. Experimental validation on financial data spanning 2010-2023 demonstrates superior performance, with 15.4% precision improvement over traditional methods and an average 2.8-day increase in detection lead time. Case studies from major market events, including the COVID-19 disruption and the 2022 volatility spike, validate the model's effectiveness in real-world scenarios. The multi-signal integration approach exhibits consistent performance across diverse market regimes, with particularly strong results during regime transitions when anomalies frequently manifest. These findings highlight the significant advantages of integrated signal processing for financial risk management and investment decision-making.