Regime-Aware Conformal Transformer for Multi-Horizon Financial Forecasting Under Market Uncertainty

Abstract

Financial forecasting systems deployed in portfolio monitoring and risk control require not only point forecasts, but also prediction intervals that remain meaningful when markets change volatility regimes. This paper proposes a Regime-Aware Conformal Transformer (RACT) for multi-horizon financial forecasting under market uncertainty. The model combines a compact Transformer encoder with asset and volatility-regime tokens, producing simultaneous return forecasts for 1-, 2-, 4-, and 8-week horizons. To quantify uncertainty, we introduce a regime-aware conformal calibration rule that computes horizon-specific volatility-normalized residual quantiles inside low-, medium-, and high-volatility regimes, while retaining a global conformal safety floor to avoid under-coverage when local volatility scaling becomes too optimistic. The resulting interval construction is conservative, causally ordered, and easy to reproduce. Experiments use the real Plotly Express stocks dataset containing weekly normalized closing prices for six technology stocks in 2018/2019. On this compact public dataset, RACT with regime-aware conformal calibration achieves 98.3% average empirical coverage at a 90% nominal level and improves 8-week coverage from 76.5% for pure volatility-scaled conformal calibration to 94.6%, at the cost of wider intervals. The results show that regime-aware conformal envelopes can materially improve long-horizon reliability, although they should be validated on larger trading datasets before production deployment.