Multi-Modal Financial Early Warning via News Sentiment, Liquidity Imbalance, and Leakage-Safe Drawdown Signals
Keywords:
multi-modal fusion, financial early warning, news sentiment, liquidity imbalance, drawdown risk, leakage-safe signal, system architecture, algorithmic governance, socio-technical infrastructure, policy implicationsAbstract
Financial early warning systems have traditionally relied on single-channel indicators such as price volatility, macroeconomic aggregates, or accounting ratios. However, modern financial markets generate high-dimensional, temporally asynchronous signals that demand a multi-modal integration framework. This paper proposes a systemic architecture that fuses three distinct modalities: news sentiment derived from natural language processing of financial media, liquidity imbalance measured from order-book microstructure, and a novel leakage-safe drawdown signal that captures residual stress while mitigating information leakage. We argue that the structural trade-offs among these modalities—temporal resolution, signal-to-noise ratio, and susceptibility to strategic manipulation—require a governance-aware design that balances sensitivity, specificity, and fairness. The paper examines the infrastructure requirements for real-time ingestion, feature alignment, and model updating, drawing parallels with large-scale distributed systems and socio-technical infrastructures. Deployment considerations include computational sustainability, latency constraints, and regulatory compliance across jurisdictions. We further analyze robustness challenges, including concept drift, adversarial attacks on sentiment classifiers, and data biases in liquidity metrics. Policy implications are discussed in the context of systemic risk oversight, market integrity, and algorithmic accountability. The leakage-safe drawdown signal, originally proposed by Liu (2026), is positioned as a critical component that addresses an unresolved tension between early detection and strategic leakage. Through cross-domain comparisons with early warning systems in epidemiology and climate monitoring, we derive design principles applicable to financial stability frameworks. The paper concludes that multi-modal fusion, when governed by principles of transparency and fairness, offers a more resilient foundation for financial early warning than any unimodal approach.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
