Quantifying Semantic Shift in Financial NLP: Robust Metrics for Market Prediction Stability

2025-09-30

AI Summary

Financial news forecasting suffers from semantic and causal drift induced by macroeconomic regime shifts, degrading model robustness across economic cycles. To address this, we propose the first drift-aware evaluation framework for financial NLP, introducing four novel metrics: Financial Causal Attribution Score, Patent Cliff Sensitivity, Temporal Semantic Volatility, and NLI Logical Consistency Score—systematically exposing model degradation mechanisms during crises. Methodologically, we integrate LSTM and Transformer architectures with feature-enhanced variants, leveraging Jensen–Shannon divergence, latent representation analysis, and GPT-4–guided case studies to quantify cross-cycle semantic stability. Experiments reveal a significant positive correlation between semantic volatility and prediction error; Transformers exhibit greater drift sensitivity, whereas feature-enhanced models demonstrate superior generalization. The framework enhances auditability and enables adaptive retraining of financial AI systems.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Financial news is essential for accurate market prediction, but evolving narratives across macroeconomic regimes introduce semantic and causal drift that weaken model reliability. We present an evaluation framework to quantify robustness in financial NLP under regime shifts. The framework defines four metrics: (1) Financial Causal Attribution Score (FCAS) for alignment with causal cues, (2) Patent Cliff Sensitivity (PCS) for sensitivity to semantic perturbations, (3) Temporal Semantic Volatility (TSV) for drift in latent text representations, and (4) NLI-based Logical Consistency Score (NLICS) for entailment coherence. Applied to LSTM and Transformer models across four economic periods (pre-COVID, COVID, post-COVID, and rate hike), the metrics reveal performance degradation during crises. Semantic volatility and Jensen-Shannon divergence correlate with prediction error. Transformers are more affected by drift, while feature-enhanced variants improve generalisation. A GPT-4 case study confirms that alignment-aware models better preserve causal and logical consistency. The framework supports auditability, stress testing, and adaptive retraining in financial AI systems.

Problem

• Quantifying semantic shift in financial NLP models
• Evaluating robustness under macroeconomic regime changes
• Measuring prediction stability across economic periods

Innovation

• Framework defines four robustness metrics for NLP
• Metrics quantify semantic drift and causal alignment
• Applied to LSTM and Transformer models across periods