LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading (2501.09636v2)

Abstract: Recent advances in deep learning and 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.

• The paper introduces LLMoE, a framework that leverages LLM-based routing to integrate multimodal data for improved financial trading decisions.
• It employs dynamic expert selection by fusing textual news with historical price data to overcome the limitations of static routing in conventional models.
• Empirical evaluations on MSFT and AAPL datasets show significant gains in Sharpe Ratio and risk management compared to traditional trading algorithms.

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

The paper "LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading" introduces a new paradigm in financial market prediction by employing LLMs as adaptive routers within a Mixture of Experts (MoE) framework, termed LLMoE. This framework specifically addresses the inherent limitations observed in conventional trading algorithms and traditional MoE models, primarily their unimodal nature and the static routing mechanism which can lead to suboptimal decision-making in volatile financial environments.

The authors posit that existing MoE systems often employ static routers—typically conventional neural networks—ineffective at harnessing the contextual richness offered by multimodal data, including textual news and historical price data. To surmount these challenges, LLMoE introduces LLM-based routers which leverage their extensive world knowledge and reasoning capabilities for expert selection.

Methodology

LLMoE's architecture consists of three core stages: LLM-based routing, expert prediction, and trading algorithm generation. The framework operates as follows:

1. LLM-Based Router: At the heart of this framework is integrating LLMs, which process and synthesize information from both historical stock prices and textual data such as news headlines. The LLM-based router provides a contextually aware, dynamic selection of experts for market prediction, a marked enhancement over static MoE routers.
2. Expert Prediction: Distinct expert models are trained to handle specific market conditions identified by the LLM router. These expert models are engineered to produce robust predictions, leveraging data classification insights drawn from the LLM router.
3. Trading Algorithm Generation: Based on expert predictions, an "All-in All-out" strategy is generated that dynamically adjusts investment positions. This strategy hinges on investing fully during predicted positive movements and liquidating during predicted negative movements, thereby optimizing for maximum returns.

Experimental Results

The paper's empirical evaluation, using datasets from Microsoft (MSFT) and Apple (AAPL), demonstrates that LLMoE significantly outperforms traditional methods and baseline models, including gradient boosting and neural networks, across all key financial metrics such as Total Return (TR), Sharpe Ratio (SR), and Maximum Drawdown (MDD). Notably, LLMoE achieves substantially improved Sharpe Ratios and Calmar Ratios, marking advances in both return performance and risk management.

Implications and Future Directions

LLMoE bridges the gap between numerical and textual data analysis, offering a more nuanced, contextually rich approach to financial market prediction. Its ability to integrate and adaptively interpret multimodal inputs significantly enhances decision-making quality over traditional models. The implications of such advancements are profound; the dynamic routing enabled by LLMs could signal a shift toward more interpretable and adaptable AI systems in financial technologies.

Looking to the future, this paper opens new pathways for refining expert selection mechanisms in MoE setups by leveraging the capabilities of LLMs. Further research could explore the application of LLMoE in other domains requiring robust pattern recognition and adaptive decision-making, potentially extending beyond financial markets. Additionally, examining the scalability and efficiency of LLMoE in real-time trading environments would provide deeper insights into its operational viability and impact.

This work contributes a significant advancement to machine learning applications in finance, reflecting broader trends toward deploying AI for dynamic and context-aware decision systems. As such, it lays the groundwork for future explorations in using LLMs to enhance predictive accuracy and interpretability in complex adaptive systems.