Paper: arXiv 2312.14903

Abstract

In this study, we developed a computational framework for simulating large-scale agent-based financial markets. Our platform supports trading multiple simultaneous assets and leverages distributed computing to scale the number and complexity of simulated agents. Heterogeneous agents make decisions in parallel, and their orders are processed through a realistic, continuous double auction matching engine. We present a baseline model implementation and show that it captures several known statistical properties of real financial markets (i.e., stylized facts). Further, we demonstrate these results without fitting models to historical financial data. Thus, this framework could be used for direct applications such as human-in-the-loop machine learning or to explore theoretically exciting questions about market microstructure’s role in forming the statistical regularities of real markets. To the best of our knowledge, this study is the first to implement multiple assets, parallel agent decision-making, a continuous double auction mechanism, and intelligent agent types in a scalable real-time environment.

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 demonstrates high mathematical complexity through its use of sophisticated stochastic processes, statistical analysis of stylized facts, and complex system dynamics. It shows substantial empirical rigor by implementing a realistic continuous double auction matching engine, running large-scale simulations with distributed computing, and validating results against known market properties without historical data fitting, making it backtest-ready and implementation-heavy.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Develop Scalable Agent-Based<br>Financial Market Simulator"] --> B["Key Methodology"]
  B --> C["Computational Processes"]
  C --> D["Key Findings"]
  
  B --> B1["Heterogeneous Agent Design<br>Multiple Asset Classes"]
  B --> B2["Distributed Computing<br>Parallel Decision-Making"]
  B --> B3["Continuous Double Auction<br>Matching Engine"]
  
  C --> C1["Simulate Large-Scale<br>Multi-Asset Markets"]
  C --> C2["Process Orders in Real-Time<br>without Historical Data"]
  
  D --> D1["Captures Stylized Facts<br>of Real Markets"]
  D --> D2["Scalable Framework for<br>ML & Market Microstructure Research"]

Cited References (70)

  1. Multifractal analysis of financial markets: a review (2019) — link
  2. Physics and financial economics (1776 \textendash 2014): puzzles, Ising and agent-based models (2014) — link
  3. Empirical properties of asset returns: stylized facts and statistical issues (2001) — link
  4. Econophysics review: I. Empirical facts (2011) — link
  5. Limit order books (2013) — link
  6. Get real (2020) — link
  7. Testing for Common Trends (1988) — link
  8. Statistical properties of stock order books: empirical results and models (2002) — link
and 62 more
  1. Radical Complexity (2021) — link
  2. Challenges, tasks, and opportunities in modeling agent-based complex systems (2021) — link
  3. Agent-based economic models and econometrics (2012) — link
  4. Time series properties of an artificial stock market (1999) — link
  5. The Predictive Power of Zero Intelligence in Financial Markets (2003) — link
  6. A multi-assets artificial stock market with zero-intelligence traders (2011) — link
  7. Agent-based simulation of a financial market (2001) — link
  8. Improving Generalization in Reinforcement Learning \textendash Based Trading by Using a Generative Adversarial Market Model (2021) — link
  9. Towards a fully rl-based market simulator (2021) — link
  10. ABIDES: Towards High-Fidelity Market Simulation for AI Research (2019) — link
  11. On the Design of Agent-Based Artificial Stock Markets (2013) — link
  12. Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model (2022) — link
  13. Simulation and estimation of an agent-based market-model with a matching engine (2021) — link
  14. Agent-Based Model Calibration using Machine Learning Surrogates (2017) — link
  15. Combining search strategies to improve performance in the calibration of economic ABMs (2023) — link
  16. Is the Juice Worth the Squeeze? Machine Learning (ML) In and For Agent-Based Modelling (ABM) (2020) — link
  17. Agent-Based Modeling for Complex Financial Systems (2018) — link
  18. How markets slowly digest changes in supply and demand (2008) — link
  19. Understanding flash crash contagion and systemic risk: A micro \textendash macro agent-based approach (2019) — link
  20. Reinforcement Learning for Market Making in a Multi-agent Dealer Market (2019) — link
  21. The Flash Crash: High-Frequency Trading in an Electronic Market (2017) — link
  22. Deep Q-learning market makers in a multi-agent simulated stock market (2021) — link
  23. An agent based model of the E-Mini SP500 applied to flash crash analysis (2012) — link
  24. Static and dynamic factors in an information-based multi-asset artificial stock market (2018) — link
  25. Multi-asset financial bubbles in an agent-based model with noise traders’ herding described by an n-vector Ising model (2021) — link
  26. DeepABM: Scalable, efficient and differentiable agent-based simulations via graph neural networks (2021) — link
  27. Th ' e orie de la sp ' e culation (1900) — link
  28. " U ber die von der molekularkinetischen Theorie der W" a rme geforderte Bewegung von in ruhenden Fl" u ssigkeiten suspendierten Teilchen (1905) — link
  29. Portfolio Selection (1952) — link
  30. The Pricing of Options and Corporate Liabilities (1973) — link
  31. An Intertemporal Capital Asset Pricing Model (1973) — link
  32. CAPITAL ASSET PRICES : A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK \ast (1964) — link
  33. The Variation of Certain Speculative Prices (1963) — link
  34. ANALYSIS OF LONG-RUN DEPENDENCE IN ECONOMICS-R/S TECHNIQUE (1971)
  35. Agent based modeling in economics and finance: past, present, and future (2022)
  36. Foundations of complexity economics (2021) — link
  37. Theory of games and economic behavior (1947)
  38. The Art of Computer Programming, The: Seminumerical Algorithms (1997)
  39. Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments (2005) — link
  40. Some Implementation Aspects of Sliding Window Least Squares Algorithms (2000) — link
  41. Random matrix approach to cross correlations in financial data (2002) — link
  42. Distribution of eigenvalues for a class of real symmetric matrices. (1971)
  43. Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems (2020) — link
  44. Econophysics: Empirical facts and agent-based models (2009) — link
  45. Scaling of the distribution of fluctuations of financial market indices (1999) — link
  46. Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982) — link
  47. Long range dependence in financial markets — link
  48. Gain/loss asymmetry in time series of individual stock prices and its relationship to the leverage effect (2009) — link
  49. Option pricing: A simplified approach (1979) — link
  50. GARCH 101: The Use of ARCH / GARCH Models in Applied Econometrics (2001) — link
  51. ARIMA Models (1990) — link
  52. Vector Autoregressions (2001) — link
  53. Noise Dressing of Financial Correlation Matrices (1999) — link
  54. RANDOM MATRIX THEORY AND FINANCIAL CORRELATIONS (2000) — link
  55. Financial Applications of Random Matrix Theory: a short review (2009) — link
  56. Proximal Policy Optimization Algorithms (2017) — link
  57. Playing Atari with Deep Reinforcement Learning (2013) — link
  58. High-Frequency Data and Models (2005) — link
  59. The expected value of the adjusted rescaled Hurst range of independent normal summands (1976) — link
  60. Julia: A fresh approach to numerical computing (2017) — link
  61. JuMP: A Modeling Language for Mathematical Optimization (2017) — link
  62. Market Microstructure Theory (1997)

0 of 70 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.