Paper: arXiv 2509.00982

Authors: Mateusz Wilinski, Juho Kanniainen

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 2.5/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper employs advanced mathematical concepts including state-space models (S5 architecture) and generative modeling, but relies entirely on synthetic data from an agent-based model rather than real market data or backtesting, with results presented as theoretical distributions rather than performance metrics.

Research Flowchart

  flowchart TD
  A["Research Goal: Imitate Trading Agents in Stock Market"] --> B["Data Input: Synthetic LOB Data<br>(Heterogeneous Agent-Based Model)"]
  B --> C["Methodology: Generative Model<br>Modified State-Space Architecture"]
  C --> D["Computational Process: Model Training"]
  D --> E["Outcome: Predicted<br>Action Distributions"]
  E --> F["Evaluation: Comparison with<br>Ground Truth (Agent-Based Model)"]
  F --> G["Key Finding: Demonstrated<br>Generative Tools for<br>Agent Imitation"]