政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/157832

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/157832

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政大典藏 > College of Commerce > Department of Money and Banking > Theses > Item 140.119/157832

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/157832

Title:	基於大型語言模型的可解釋性混合專家系統：多模態資料驅動的創新自動交易框架 Interpretable Mixture of Experts Based on Large Language Models: A Multimodal Data-Driven Framework for Innovative Automated Trading
Authors:	劉冠銘 Liu, Kuan-Ming
Contributors:	江彌修 Chiang, Mi-Hsiu 劉冠銘 Liu, Kuan-Ming
Keywords:	大型語言模型混和專家模型動態交易策略 Large Language Models Mixture-of-Experts Dynamic Trading Strategies
Date:	2025
Issue Date:	2025-07-01 15:16:48 (UTC+8)
Abstract:	隨著深度學習與大型語言模型(LLMs) 的快速發展，專家混合模型(Mixture-of-Experts, MoE) 在股票投資領域的應用得到了推動。雖然這些模型展現出優秀的交易績效，但大多數仍局限於單模態數據處理，忽略了來自文本數據等其他模態所提供的豐富信息。另外傳統基於神經網絡的路由器選擇機制無法充分考慮上下文和現實世界的細微差異，導致專家選擇次優化。為了解決這些問題，本研究提出了一種新穎框架，通過將大型語言模型作為路由器融入MoE 架構，充分利用大型語言模型預訓練好的的世界知識與推理能力，動態選擇專家來處理歷史價格數據與股票新聞。這種方法不僅提升了專家選擇的效率與準確性，還增強了模型的解釋性。實驗結果顯示，基於多模態真實股票數據的本研究模型框架在總回報率(TR)、夏普比率(SR) 和卡爾瑪比率(CR) 等多項核心指標上顯著優於傳統MoE 模型及其他深度神經網絡方法。LLMoE 通過將數值數據與文本數據整合，實現了更有效的專家選擇與交易決策，並展現出卓越的風險調整能力。此外，該框架靈活的架構設計可輕鬆適配於多種下游任務，其高透明性則通過自然語言推理增強了交易決策的可信度。綜上所述，本研究提供了一個創新的智能交易解決方案，彌補了傳統模型的不足，並為未來在金融市場的應用與研究開闢了新方向。 With the rapid development of deep learning and large language models (LLMs), the application of Mixture-of-Experts (MoE) models in stock investment has gained momentum. While these models demonstrate excellent trading performance, most remain limited to single-modal data processing, overlooking the rich information provided by other modalities such as textual data. Additionally, traditional neural network-based router selection mechanisms fail to adequately consider contextual and real-world nuances, leading to suboptimal expert selection. To address these issues, this study proposes a novel framework that incorporates large language models as routers within the MoE architecture, leveraging the pre-trained world knowledge and reasoning capabilities of LLMs to dynamically select experts for processing historical price data and stock news. This approach not only improves the efficiency and accuracy of expert selection but also enhances model interpretability. Experimental results show that the proposed model framework, based on multimodal real stock data, significantly outperforms traditional MoE models and other deep neural network methods across multiple core metrics including Total Return (TR), Sharpe Ratio (SR), and Calmar Ratio (CR). LLMoE achieves more effective expert selection and trading decisions by integrating numerical data with textual data, demonstrating superior risk-adjusted performance. Furthermore, the framework's flexible architectural design can be easily adapted to various downstream tasks, while its high transparency enhances the credibility of trading decisions through natural language reasoning. In conclusion, this study provides an innovative intelligent trading solution that addresses the shortcomings of traditional models and opens new directions for future applications and research in financial markets.
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Description:	碩士國立政治大學金融學系 112352014
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0112352014
Data Type:	thesis
Appears in Collections:	[Department of Money and Banking] Theses

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