Taming the Trading Floor: How 'Roaree' Optimizers Could Redefine AI Stock Forecasting
When financial AI meets the optimizer arms race, the stakes are measured in both milliseconds and market moves. The recent From Rattle to Roar study tests this premise with MambaStock — a selective state-space model — trained to forecast S&P 500 weekly returns. The twist: pitting eight widely-used optimizers against a new family called Roaree, designed to capture Lion’s speed while taming its instability. Why Optimizers Matter More in Finance Than You Think In financial forecasting, milliseconds can mean the difference between execution and regret. This makes optimizer choice not just a theoretical concern but a practical lever for profitability. The study reinforces that adaptive-rate, momentum-based methods (Adam, RMSProp, Nesterov) deliver the lowest test errors for noisy, small-magnitude financial returns. Vanilla SGD struggles in this regime; AdamW’s decoupled weight decay over-regularizes, slowing convergence in already weak-signal environments. ...