Evaluating Loss Functions for Stock Ranking: An Empirical Analysis With Transformer Model

Battle Count: 343

Highly relevant to quantitative trading. The paper directly addresses a critical practical question: which loss function to use when training ML models for stock ranking in portfolio construction. It provides actionable guidance (Margin, ListNet, BPR outperform MSE) and benchmarks 8 loss functions on realistic S&P 500 data with portfolio-level metrics. The finding that ranking quality metrics (IC) don't perfectly predict portfolio performance is particularly valuable for practitioners.

Moderate complexity. The PortfolioMASTER architecture with spatio-temporal attention is non-trivial to implement. The loss functions themselves are well-defined and implementable, but the combined loss framework (Eq. 2) with hyperparameter λ tuning adds complexity. Grid search over multiple hyperparameters (model dimension, layers, attention heads, dropout, loss-specific parameters) requires significant computational resources. The portfolio simulation and evaluation pipeline is straightforward.

The paper provides detailed methodology, loss function formulas, hyperparameter tuning descriptions, and dataset specifications (110 S&P 500 stocks, 2015-2024, T=20 lookback, F=2 features). However, no GitHub repository or code link is provided. Hyperparameters were tuned via grid search but exact values are not fully reported. The PortfolioMASTER architecture is described but not fully specified in code.