LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading

Paper: arXiv 2501.09636

Abstract

Recent advances in deep learning and large language models (LLMs) have facilitated the deployment of the mixture-of-experts (MoE) mechanism in the stock investment domain. While these models have demonstrated promising trading performance, they are often unimodal, neglecting the wealth of information available in other modalities, such as textual data. Moreover, the traditional neural network-based router selection mechanism fails to consider contextual and real-world nuances, resulting in suboptimal expert selection. To address these limitations, we propose LLMoE, a novel framework that employs LLMs as the router within the MoE architecture. Specifically, we replace the conventional neural network-based router with LLMs, leveraging their extensive world knowledge and reasoning capabilities to select experts based on historical price data and stock news. This approach provides a more effective and interpretable selection mechanism. Our experiments on multimodal real-world stock datasets demonstrate that LLMoE outperforms state-of-the-art MoE models and other deep neural network approaches. Additionally, the flexible architecture of LLMoE allows for easy adaptation to various downstream tasks.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 6.0/10 (capped from 7.5: transaction costs not modelled; see penalty flags)
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper involves advanced ML architecture (MoE with LLM router) but the mathematics is presented at a conceptual level without dense derivations, keeping complexity moderate. Empirical rigor is high due to the use of real-world datasets (MSFT, AAPL), detailed feature engineering, backtesting metrics (Sharpe, Sortino, MDD, etc.), and multiple baselines.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Improve MoE Routing for Stock Investment<br>Integrating Multimodal Data & Context"] --> B["Data Collection<br>Historical Price & Stock News"]
  B --> C["Framework: LLMoE<br>LLM-as-Router replaces NN Router"]
  C --> D["Input Processing<br>Multimodal Features"]
  D --> E["LLM Reasoning & Routing<br>Select Experts based on Context"]
  E --> F["Expert Execution<br>Specialized Models Process Data"]
  F --> G["Output Prediction<br>Stock Prediction / Trading Signal"]
  G --> H["Key Findings<br>Outperforms SOTA MoE & DNNs<br>Better Interpretability & Flexibility"]

Cited References (15)

1. Mastering Stock Markets with Efficient Mixture of Diversified Trading Experts (2023) — link
2. Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts (2021) — link
3. Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations (2020) — link
4. A Deep Learning Framework for News-oriented Stock Trend Prediction (2018) — link
5. MIGA: Mixture-of-Experts with Group Aggregation for Stock Market Prediction (2024) — link
6. An Attention-Based Multi-Gate Mixture-of-Experts Model for Quantitative Stock Selection (2023) — link
7. Automate Strategy Finding with LLM in Quant investment (2024) — link
8. Artificial Intelligence in Accounting and Finance: Challenges and Opportunities (2023) — link

1 of 15 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.