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

ArXiv ID: 2508.04707 “View on arXiv”

Authors: Alena Chan, Maria Garmonina

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.

Keywords: Optimizer, Gradient Smoothing, Adaptive-Rate, MambaStock, Roaree, Equities

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper introduces new optimizer variants with a few novel activation functions (e.g., erf), but the core methodology is standard hyperparameter tuning. Empirical rigor is high due to detailed backtesting on S&P 500 data, inclusion of multiple error metrics (MSE, directional accuracy), and a reproducible GitHub link, though it lacks live trading validation.

  flowchart TD
    A["Research Goal: Find optimal optimizer for MambaStock on S&P 500"] --> B["Data: S&P 500 Historical Price Data"]
    B --> C["Model: MambaStock for Return Forecasting"]
    C --> D["Training: Parallel Comparison of Optimizers"]
    D --> E{"Evaluation: Test Error & Speed"}
    E --> F["Findings: Adam/RMSProp (Lowest Error)"]
    E --> G["Findings: Lion (Fastest Training)"]
    F & G --> H["Outcome: Roaree Optimizer Family<br/>(Balances Speed & Stability)"]