This study evaluates multiple forecasting models, ranging from HAR and GARCH to Tree-based and Neural architectures, across 14 Global Equity Indices and Horizons form 1 day to 100 Trading days within a strictly chronological and capacity-controlled framework, indicating that for strongly dependent time series, nominal sample size is a misleading measure of learnability, and model capacity must be constrained relative to effective information rather than observation count.
This paper proposes RG-ResMoE, a regime-gated residual mixture-of-experts architecture in which regime information is used only for expert routing rather than for direct forecasting, which consistently outperforms a capacity-matched MLP in both forecasting accuracy and training stability in the main U.S. study.
SHAP analysis reveals that trading activity, lagged liquidity, and market uncertainty are the main determinants of liquidity forecasts, and the findings highlight the complementary role of explainable machine learning in empirical finance.
Veni Arakelia, G. Caporale, Mirto M Gasparinatou et al.· CESifo working papers· 0 citations
Forward-looking volatility forecasts are central inputs to derivatives pricing, market making, risk management, and volatility-linked trading strategies, with ARCH and GARCH models serving as the canonical workhorses. Such models are natural in standard asset markets, where prices are positive-valued stochastic process...
Weiye Xi, C. Moallemi, Mallesh M. Pai et al.· 0 citations
Direct-yield models perform best for slope forecasts and are relatively stronger at short horizons, while factor-based models become more competitive at longer horizons, while macroeconomic predictors provide clear incremental predictive power, strongest for slope-related movements.
This paper utilises high‐frequency data from the Chinese stock market and a panel of individual stocks to compare the forecasting performance of machine learning with popular econometric models across different periods and indicates that over a longer forecasting horizon, this method achieves the best performance among...
