Regime-Conditional Distributional Comparison of Trading Strategies: A GAMLSS/ZAGA Framework Applied to the S&P 500

Battle Count: 68

Directly addresses a core problem in quantitative trading: how to rigorously compare algorithmic strategies beyond single scalar metrics. The regime-conditional distributional framework provides actionable insights about when (under which market conditions) an ML-based strategy outperforms a passive benchmark. The walk-forward methodology, SVMP strategy, and Adjusted Information Ratio are all standard in quantitative trading practice. The finding that strategy dominance is regime-dependent has direct implications for adaptive strategy selection and portfolio construction.

Requires expertise in: (1) GAMLSS framework and ZAGA distribution fitting in R, (2) walk-forward backtesting with proper train/test splits, (3) SVM with polynomial kernel for return prediction, (4) parametric bootstrap with 9,999 replications, (5) handling near-complete separation in logistic components, (6) analytical derivation of moments from ZAGA parameters. The R packages (gamlss, gamlss.dist, e1071) are well-documented, but the overall pipeline integration and interpretation of results require significant statistical and financial expertise.

The paper specifies all R packages used (gamlss, gamlss.dist, e1071, quantmod, TTR), provides detailed parameter settings for the SVMP model (p=7, c=2, d=2), describes the walk-forward procedure with exact window sizes (T_IS=160, T_OOS=40, step=40), and reports full estimated coefficients. Data source (Yahoo Finance S&P 500) is publicly accessible. However, no code repository is provided, and the exact random seed for bootstrap is not specified. The author acknowledges using Claude (Anthropic) for R script assistance.