Regime-based evaluation framework for financial NLP stability. Implements chronological cross-validation, semantic drift quantification via Jensen-Shannon divergence, and multi-faceted robustness profiling. Replicates Sun et al.'s (2025) methodology with modular, auditable Python codebase.
natural-language-processing deep-learning reproducible-research transformers pytorch lstm quantitative-finance evaluation-metrics stock-prediction time-series-analysis market-analysis distilbert model-robustness sentence-transformers financial-nlp ai-in-finance model-governance financial-news-analysis regime-detection semantic-shift
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Updated
Oct 3, 2025 - Jupyter Notebook