Temporal Resolution and Model Specification in Release-Aware U.S. REIT ETF Return Forecasting

This study examines whether higher temporal resolution and more flexible model specification improve short-horizon forecasting of aggregate U.S. listed-real-estate exchange-traded fund returns. Using release-aware daily, monthly, and quarterly predictors, we evaluate VNQ as the primary target and IYR as a closely related robustness proxy over one-, two-, and four-week horizons, with a fixed 2022–2025 out-of-sample period. Seven forecasting systems are compared, including a Historical Mean benchmark, ARX-Ridge, Weekly LSTM, MIDAS-ADL, Mixed-Frequency VAR, Frequency-Specific LSTM, and Cross-Frequency Attention. Weekly-versus-native comparisons are interpreted as system-level contrasts, while a separate matched daily predictor experiment provides a narrower sensitivity check on sampling resolution. The Historical Mean achieves the lowest RMSE in all six target–horizon cases. The system-level comparisons show no consistent advantage for native-frequency implementations, and the matched experiment likewise provides no general evidence that retaining daily resolution improves forecasts. MIDAS-ADL exhibits specification-specific long-horizon instability associated with amplified fitted contributions, particularly from the monthly block, although annual re-estimation substantially attenuates this behavior. Forecast performance, therefore, depends jointly on temporal representation, model specification, information integration, and updating policy.