Executing Large Financial Orders Using Language Models
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Key Takeaways
- The paper introduces a hierarchical framework called Plan-Ahead Controlled Execution that splits parent-order execution into long-horizon planning and short-horizon execution.
- Using DeepSeek-v4-flash, the framework improves value-weighted price performance by 1.02 basis points under aggressive order submission and 1.07 basis points under passive submission compared to a standard time-weighted baseline.
- For a fund trading USD 100 billion annually, this cost reduction corresponds to approximately USD 10 million in annual savings.
- Ablation experiments confirm that removing key components of the framework reduces performance gains, highlighting the value of both planning and execution stages.
Summary & Methodology Analysis
When financial institutions need to buy or sell massive quantities of a stock, executing the entire trade at once creates massive price shifts that hurt profitability. To solve this, traders split large orders into smaller pieces over time using standard static strategies like time-weighted average price, which divides the total quantity uniformly across pre-set time slots, or learning-based strategies that require task-specific training. This paper explores whether large language models, artificial intelligence systems trained on vast amounts of text, can handle this order-splitting process dynamically without needing custom training or rigid market assumptions.
The proposed solution is a hierarchical framework named Plan-Ahead Controlled Execution, which bridges long-term planning with short-term adjustments. Given a parent order and past market history, a long-horizon planner model outputs a trend assessment, quantity preference scores across time slots, and a confidence score. These scores are blended with a reference schedule using a hyperparameter control. Next, a short-horizon executor model looks at recent market history and outputs a quantity adjustment score for immediate trades. The system is tested using a matching environment with aggressive and passive order-submission settings on Shenzhen Stock Exchange Level-1 Snapshot data from April 2026.
Despite outperforming traditional baselines, the approach has several limitations highlighted in the paper. The framework is sensitive to hyperparameter choices, meaning improper settings can cause models to either stay too close to simple baselines or overreact to noise. Additionally, frequent replanning hurts shorter parent orders by disrupting the trading pace, and evaluation is currently restricted to backtesting rather than live trading. The paper also notes that stock IDs and trading dates are removed from model inputs to prevent information leakage, and results may vary in markets without short-sale limits like those in China.
Interactive System Flowchart
Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the main problem addressed in this paper?
The paper addresses parent-order execution in algorithmic trading, where splitting a large order into smaller orders is necessary to reduce execution costs while avoiding the rigid assumptions of static strategies and the complex training requirements of learning-based strategies.
Q2. What is the core framework proposed by the authors?
The authors propose a hierarchical framework named Plan-Ahead Controlled Execution, which decomposes parent-order execution into long-horizon planning and short-horizon execution.
Q3. How much money could this framework potentially save a large fund?
For a fund trading USD 100 billion annually, replacing a standard time-weighted baseline with this framework reduces execution costs by about one basis point, corresponding to approximately USD 10 million in annual savings.
Q4. What models and datasets were evaluated in the study?
The study evaluated ChatGPT-5.4 and DeepSeek-v4-flash using Shenzhen Stock Exchange Level-1 Snapshot data from April 2026, alongside traditional baselines like time-weighted average price, Almgren-Chriss, XGBoost, and LSTM.
Q5. What are the main hyperparameters and settings used in the framework?
The main hyperparameters are lambda set to 0.3, gamma set to 0.5, a short-horizon window tau of 5 minutes, and a time slot Delta of 1 minute. Each parent order is repeated 8 times to mitigate model stochasticity.
Q6. How does the short-horizon executor adjust trading quantities?
The short-horizon executor takes sub-plans, market history, trend assessments, and a baseline schedule to output a quantity adjustment score that modifies the baseline trading volume.
Q7. What were the results of the ablation study?
The ablation study under the aggressive setting showed that the full model achieved a value-weighted price performance of -2.26 basis points, whereas removing components like the planner or execution adjustments resulted in lower performance gains.
Q8. What limitations do the authors mention regarding replanning windows?
Planner replanning hurts shorter parent orders because frequent replanning disrupts the trading pace and introduces extra noise, whereas one-shot planning becomes harder for longer parent orders due to unpredictable future trends.
Q9. Has this framework been tested in live trading?
No, the evaluation is based entirely on backtesting environments, and future work is suggested to evaluate the framework in live trading.