From Rattle to Roar: Optimizer Showdown for MambaStock on S&P 500

Alena Chan and Maria Garmonina

Papers from arXiv.org

Abstract: We evaluate the performance of several optimizers on the task of forecasting S&P 500 Index returns with the MambaStock model. Among the most widely used algorithms, gradient-smoothing and adaptive-rate optimizers (for example, Adam and RMSProp) yield the lowest test errors. In contrast, the Lion optimizer offers notably faster training. To combine these advantages, we introduce a novel family of optimizers, Roaree, that dampens the oscillatory loss behavior often seen with Lion while preserving its training speed.

Date: 2025-07
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