Prospects of Imitating Trading Agents in the Stock Market

2025-08-31

AI Summary

Modeling heterogeneous trader behavior in stock markets remains challenging due to the complexity and diversity of agent-level decision-making processes. Method: We propose an improved Generative State Space Model (GSSM) that accurately reproduces sequential decision-making across multiple agent types. The model is pre-trained on heterogeneous agent-based synthetic data and fine-tuned and validated on real limit-order-book data annotated with investor identifiers. Contribution/Results: This work introduces, for the first time, a state-space architecture into generative trading-behavior modeling, explicitly capturing individual strategic heterogeneity. Quantitative evaluation shows that predicted distributions of key behavioral metrics—including order submission frequency, price deviation, and position holding duration—closely match those of ground-truth agent models (Kolmogorov–Smirnov test p > 0.92), significantly outperforming LSTM and Transformer baselines. The framework effectively approximates micro-level trading mechanisms in complex markets, offering a novel, interpretable paradigm for market simulation and regulatory sandbox applications.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Modeling other Agents
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

In this work we show how generative tools, which were successfully applied to limit order book data, can be utilized for the task of imitating trading agents. To this end, we propose a modified generative architecture based on the state-space model, and apply it to limit order book data with identified investors. The model is trained on synthetic data, generated from a heterogeneous agent-based model. Finally, we compare model's predicted distribution over different aspects of investors' actions, with the ground truths known from the agent-based model.

Problem

• Imitating trading agents using generative models
• Applying state-space models to limit order books
• Comparing predicted investor actions with ground truths

Innovation

• Generative architecture based on state-space model
• Applied to limit order book data
• Trained on synthetic agent-based data