Language models can be trained to output financial predictions and portfolio weights as constrained tokens, matching or beating traditional regression-based approaches while maintaining a unified model architecture.
This paper shows that language models can directly generate financial forecasts and investment decisions as tokens, rather than using separate prediction heads. FinATOM uses constrained token generation to predict stock returns and allocate across ETFs, achieving better risk-adjusted returns than traditional approaches when trained with ranking supervision and reinforcement learning.