What Teaches Robots to Walk, Teaches Them to Trade too – Regime Adaptive Execution using Informed Data and LLMs

ArXiv ID: 2406.15508 “View on arXiv”

Authors: Unknown

Abstract

Machine learning techniques applied to the problem of financial market forecasting struggle with dynamic regime switching, or underlying correlation and covariance shifts in true (hidden) market variables. Drawing inspiration from the success of reinforcement learning in robotics, particularly in agile locomotion adaptation of quadruped robots to unseen terrains, we introduce an innovative approach that leverages world knowledge of pretrained LLMs (aka. ‘privileged information’ in robotics) and dynamically adapts them using intrinsic, natural market rewards using LLM alignment technique we dub as “Reinforcement Learning from Market Feedback” (RLMF). Strong empirical results demonstrate the efficacy of our method in adapting to regime shifts in financial markets, a challenge that has long plagued predictive models in this domain. The proposed algorithmic framework outperforms best-performing SOTA LLM models on the existing (FLARE) benchmark stock-movement (SM) tasks by more than 15% improved accuracy. On the recently proposed NIFTY SM task, our adaptive policy outperforms the SOTA best performing trillion parameter models like GPT-4. The paper details the dual-phase, teacher-student architecture and implementation of our model, the empirical results obtained, and an analysis of the role of language embeddings in terms of Information Gain.

Keywords: Reinforcement Learning from Market Feedback (RLMF), LLM Alignment, Regime Switching, World Knowledge, Adaptive Policy, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper involves advanced ML architectures (teacher-student RL, LLM alignment) and partial observable MDP formalization, while providing strong empirical results on real benchmarks (FLARE, NIFTY) with reported accuracy improvements over SOTA models.

  flowchart TD
    A["Research Goal: Adapt ML to Market Regime Shifts<br/>Inspired by Robot Locomotion"] --> B{"Methodology: Regime Adaptive Execution"}
    B --> C["Input: World Knowledge<br/>Pre-trained LLM Teacher"]
    B --> D["Input: Intrinsic Market Rewards<br/>(Price Data)"]
    C --> E["Teacher: LLM Alignment via RLMF<br/>Reinforcement Learning from Market Feedback"]
    D --> E
    E --> F["Output: Adaptive Policy<br/>Student Model"]
    F --> G{"Evaluation"}
    G --> H["Strong Empirical Results"]
    H --> I["15%+ Accuracy Gain vs SOTA on FLARE<br/>Outperforms GPT-4 on NIFTY SM Task"]