Transfer Learning for Loan Recovery Prediction under Distribution Shifts with Heterogeneous Feature Spaces

Battle Count: 90

The paper focuses on credit risk recovery rate modeling and regulatory capital determination rather than trading strategies. While recovery rate predictions inform portfolio risk and capital allocation decisions relevant to fixed-income and credit trading desks, the methodology is not directly applicable to quantitative trading signals, market microstructure, or algorithmic execution. The transfer learning and distributional forecasting techniques could potentially be adapted for credit spread prediction or distressed debt valuation.

High complexity due to: (1) FT-Transformer backbone with feature-wise tokenization, (2) Mixture Density Network head with bounded-support parameterization, (3) Two-stage transfer learning pipeline with schema masking and PAD tokens, (4) Handling of heterogeneous feature spaces with separate encoder banks for shared/task-specific features, (5) Categorical embedding with union vocabulary construction, (6) Monte Carlo simulation framework for controlled shift generation, (7) Multiple evaluation scenarios (zero-shot, target-baseline, transfer). Requires careful hyperparameter tuning and understanding of both Transformer architectures and probabilistic modeling.

Complete code, configuration files, and scripts available in GitHub repository. Monte Carlo simulation framework is fully reproducible with fixed random seeds. However, GCD data access requires consortium membership, and UP5 data availability is limited to NUS affiliation.