TABL-ABM: A Hybrid Framework for Synthetic LOB Generation

Paper: arXiv 2510.22685

Authors: Ollie Olby, Rory Baggott, Namid Stillman

Abstract

The recent application of deep learning models to financial trading has heightened the need for high fidelity financial time series data. This synthetic data can be used to supplement historical data to train large trading models. The state-of-the-art models for the generative application often rely on huge amounts of historical data and large, complicated models. These models range from autoregressive and diffusion-based models through to architecturally simpler models such as the temporal-attention bilinear layer. Agent-based approaches to modelling limit order book dynamics can also recreate trading activity through mechanistic models of trader behaviours. In this work, we demonstrate how a popular agent-based framework for simulating intraday trading activity, the Chiarella model, can be combined with one of the most performant deep learning models for forecasting multi-variate time series, the TABL model. This forecasting model is coupled to a simulation of a matching engine with a novel method for simulating deleted order flow. Our simulator gives us the ability to test the generative abilities of the forecasting model using stylised facts. Our results show that this methodology generates realistic price dynamics however, when analysing deeper, parts of the markets microstructure are not accurately recreated, highlighting the necessity for including more sophisticated agent behaviors into the modeling framework to help account for tail events.

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper combines advanced mathematical concepts from deep learning (TABL models) and agent-based modeling (Chiarella model) with stylized facts and simulation outputs to validate its synthetic LOB generation.

Research Flowchart

  flowchart TD
  A["Research Goal: Improve synthetic LOB generation using hybrid AI methods"] --> B{"Key Methodology: TABL-ABM Framework"}
  B --> C["Chiarella Model<br/>(Agent-Based Simulation)"]
  B --> D["TABL Model<br/>(Time Series Forecasting)"]
  C --> E["Matching Engine &<br/>Deleted Order Flow"]
  D --> E
  E --> F{"Stylized Facts Analysis<br/>vs. Real Market Data"}
  F --> G["Outcomes: Realistic Price Dynamics"]
  F --> H["Outcome: Inaccurate Microstructure & Tail Events"]

Cited References (32)

1. Financial Time Series Forecasting using CNN and Transformer (2023) — link
2. DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2019) — link
3. Modality-aware Transformer for Financial Time series Forecasting (2024) — link
4. Prediction of Stock Price Time Series using Transformers (2023) — link
5. Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis (2019) — link
6. Data Normalization for Bilinear Structures in High-Frequency Financial Time-series (2020) — link
7. Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction (2022) — link
8. Metadata Matters for Time Series: Informative Forecasting with Transformers (2024) — link

0 of 32 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.