Quantifying the Risk–Return Tradeoff in Forecasting

Battle Count: 50

The paper directly imports risk-adjusted performance metrics from finance (Sharpe, Sortino, Omega, drawdown) and applies them to forecast evaluation. The framework is highly relevant for quantitative trading in several ways: (1) evaluating macroeconomic forecasts used in trading strategies, (2) assessing the reliability of ML-based alpha signals, (3) the Edge Ratio concept maps to identifying unique alpha sources, (4) the meta-analysis framework applies to evaluating trading strategies across different market regimes. The paper's core insight—that average accuracy differs from reliability—directly parallels the distinction between mean returns and risk-adjusted returns in portfolio management. However, the paper focuses on macroeconomic forecasting rather than direct trading applications.

The core risk-adjusted metrics (Sharpe, Sortino, Omega, drawdown) are straightforward to compute given loss differentials. The Edge Ratio requires computing the frontier across all models at each time point. The meta-analysis extension adds cross-sectional aggregation. However, the full empirical application involves 10+ models, multiple horizons, expanding-window estimation, and two evaluation periods, requiring substantial computational infrastructure. The HNN and LGB+ models are from the author's prior work and may require custom implementations. TabPFN requires the specific transformer architecture. Overall, the framework is conceptually simple but the full application is moderately complex.

The paper uses publicly available data (FRED-QD database, Survey of Professional Forecasters, M4 competition dataset). Model specifications are detailed in the appendix. However, some models (HNN, LGB+, TabPFN) are from the author's own prior work and may require specific implementations. The recursive out-of-sample design with expanding windows and re-estimation every 8 quarters is clearly described. Full results tables are provided in the appendix.