Paper: arXiv 2508.04707

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.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: 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.

Research Flowchart

  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)"]