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

Battle Count: 68

Highly relevant to quantitative trading as it directly addresses model reliability under regime shifts, which is a critical challenge for ML-based trading strategies. The framework provides actionable diagnostics for when to retrain, adapt, or reject models during market transitions. The finding that Transformers degrade severely during crises (MSE 40.95 vs LSTM 3.74) is directly applicable to strategy robustness. The regime-aware evaluation supports stress testing of trading models. However, the paper focuses on evaluation rather than proposing improved trading strategies or execution algorithms.

Moderate complexity. Requires training multiple model architectures (LSTM, DistilBERT, feature-enhanced Transformer), computing four custom diagnostic metrics, performing Jensen-Shannon divergence calculations on TF-IDF distributions, and integrating GPT-4 API calls for NLICS evaluation. The pipeline involves data preprocessing (TF-IDF, MiniLM embeddings), chronological splitting, regime assignment, and multi-metric evaluation. GPT-4 integration adds API dependency and cost. However, individual components are well-defined and use standard libraries (PyTorch, HuggingFace Transformers).

The paper provides detailed hyperparameters (learning rate 0.001, batch size 64, hidden size 256, dropout 0.2), data sources (FNSPID, Yahoo Finance), model architectures, and algorithm pseudocode. However, no GitHub repository or code link is provided. GPT-4 API version is specified (April 2024). The regime windows and sector composition are clearly defined. Reproducibility is moderate due to reliance on proprietary GPT-4 API and specific dataset versions.