Introducing the CZAR Loss: A Tailored Objective Function for Financial Log-Return Predictions

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Directly addresses a fundamental flaw in standard ML training for finance (zero-returns bias) and provides a drop-in replacement for MSE/MAE in gradient-boosted models, improving directional skill and Sharpe ratios.

Moderate. Requires implementing a custom objective function with gradient and Hessian for LightGBM/XGBoost. The code is provided, but understanding the piecewise logic and hyperparameter defaults requires careful reading.

The paper provides a complete Python reference implementation in Appendix A, specifies hyperparameters, data sources (Tiingo), and experimental setup details (LightGBM parameters, feature engineering).