From Rattle to Roar: Optimizer Showdown for MambaStock on S&P 500
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908480737116160 |
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| author | Chan, Alena Garmonina, Maria |
| author_facet | Chan, Alena Garmonina, Maria |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04707 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Rattle to Roar: Optimizer Showdown for MambaStock on S&P 500 Chan, Alena Garmonina, Maria Computational Finance Machine Learning 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. |
| title | From Rattle to Roar: Optimizer Showdown for MambaStock on S&P 500 |
| topic | Computational Finance Machine Learning |
| url | https://arxiv.org/abs/2508.04707 |