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"]

Cited References (20)

  1. Financial markets as nonlinear adaptive evolutionarysystems (2001)
  2. Agent-based modeling (2012)
  3. Generative ai for end-to-end limit order book modelling: A token-level autoregressive generative model of message flow using a deep state space network (2023)
  4. ABIDES: Towards high-fidelity multi-agent market simulation (2020)
  5. Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects (2021)
  6. JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading (2023)
  7. Black-box Bayesian inference for economic agent-based models (2022) — link
  8. A simulation analysis of the microstructure ofdouble auction markets (2002)
and 12 more
  1. An empirical behavioral model of liquidity and volatility (2008)
  2. Data-driven economic agent-based models (2024) — link
  3. On learning agent-based models from data (2023)
  4. Calibrating Agent-based Models to Microdata with Graph Neural Networks
  5. MarketGPT: Developing a Pre-trained transformer (GPT) for Modeling Financial Time Series — → 2411.16585
  6. Classifying and Clustering Trading Agents — → 2505.21662
  7. A generative model of a limit order book using recurrent neural networks (2023)
  8. LOB-Bench: Benchmarking Generative AI for Finance–an Application to Limit Order Book Data — → 2502.09172
  9. Simplified state space layers for sequence modeling (2022) — link
  10. Attention is all you need (2017)
  11. Time series analysis by state space methods (2012)
  12. Adam: A method for stochastic optimization (2014) — link

3 of 20 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.