大型語言模型能否執行母單？

摘要

母單執行是演算法交易中的核心問題，其目標是將大額訂單拆分為較小的訂單，同時降低執行成本。現有方法要么依賴於可能無法在實務中成立的預設市場假設，要么需要限於特定任務的訓練，從而限制了其對新情境的適應能力。為克服這些限制，我們提出了首個針對大型語言模型用於母單執行的系統性研究。這將大型語言模型在金融領域的應用從「交易什麼」擴展到「如何執行」。我們提出PACE（前瞻規劃受控執行），這是一個將母單執行分解為長期規劃與短期執行的分層框架，既不需要明確的市場假設，也不需要特定任務的訓練。在深圳證券交易所Level-1資料上的實驗表明，PACE優於TWAP、Almgren-Chriss以及基於學習的基線方法，比最強基線高出0.65個基點。行為分析揭示，大型語言模型做出執行決策的方式與人類投資者不同：較高的模型信心預示著更好的表現，而非更差的報酬，且模型會提前交易，而非拖延至截止時間。這些發現表明，大型語言模型可以在執行決策中補充人類交易員的不足。

English

Parent-order execution is a core problem in algorithmic trading, where the goal is to split a large order into smaller orders while reducing execution costs. Existing approaches either rely on pre-specified market assumptions that may not hold in practice, or require task-specific training that limits adaptability to new settings. To overcome these limitations, we present the first systematic study of large language models (LLMs) for parent-order execution. This extends the use of LLMs in finance from what to trade to how to execute. We propose PACE (Plan-Ahead Controlled Execution), a hierarchical framework that decomposes parent-order execution into long-horizon planning and short-horizon execution, requiring neither explicit market assumptions nor task-specific training. Experiments on Shenzhen Stock Exchange Level-1 data show that PACE outperforms TWAP, Almgren-Chriss, and learning-based baselines, exceeding the strongest baseline by 0.65 bps. Behavioral analysis reveals that LLMs make execution decisions differently from human investors: higher model confidence predicts better performance rather than worse returns, and the model trades earlier rather than procrastinating toward the deadline. These findings suggest that LLMs can complement human traders in execution decisions.