Financial time-series forecasting must capture price dynamics across heterogeneous assets while incorporating news available at prediction time.
We introduce DualCast, a dual-path framework that extends a frozen language model with a discrete financial vocabulary.
Each log-return patch is represented by a learned summary token and three residual shape tokens, preserving local drift and volatility while allowing shape patterns to be shared across assets.
To improve codebook utilization, we develop adaptive frequency-equalizing residual vector quantization, which rebalances overloaded codewords without compromising reconstruction accuracy.
The fast path trains only the new financial-token embeddings and output heads on a frozen Qwen3-8B backbone.
A toggleable LoRA adapter enables a slow path that conditions on the fast forecast and news available at the forecast origin to produce a revised prediction.
The reviser is initialized by supervised fine-tuning and further optimized with a return-space group relative policy optimization objective that rewards improvements over the fast forecast.
In zero-shot evaluations covering equities and energy prices at five-minute, daily, and weekly resolutions, the slow path achieves the lowest mean absolute percentage error among the compared methods in 8 of 12 dataset-horizon settings, including every longest-horizon setting.
News ablations indicate additional gains in most tested settings, although their magnitude varies across markets.
DualCast thus combines a fast numerical forecaster with an optional text-conditioned revision mechanism.