Simulating Liquidity: Agent-Based Modeling of Illiquid Markets for Fractional Ownership

ArXiv ID: 2411.13381 “View on arXiv”

Authors: Unknown

Abstract

This research investigates liquidity dynamics in fractional ownership markets, focusing on illiquid alternative investments traded on a FinTech platform. By leveraging empirical data and employing agent-based modeling (ABM), the study simulates trading behaviors in sell offer-driven systems, providing a foundation for generating insights into how different market structures influence liquidity. The ABM-based simulation model provides a data augmentation environment which allows for the exploration of diverse trading architectures and rules, offering an alternative to direct experimentation. This approach bridges academic theory and practical application, supported by collaboration with industry and Swiss federal funding. The paper lays the foundation for planned extensions, including the identification of a liquidity-maximizing trading environment and the design of a market maker, by simulating the current functioning of the investment platform using an ABM specified with empirical data.

Keywords: liquidity dynamics, agent-based modeling (ABM), fractional ownership, market structure, data augmentation, Alternative Investments

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders
  • Why: The paper leverages real-world data from a FinTech platform and employs agent-based modeling with empirical parameterization, showing strong empirical rigor focused on practical implementation. The mathematical complexity is moderate, focusing on simulation logic rather than dense theoretical derivations or heavy statistical modeling.
  flowchart TD
    A["Research Goal:<br/>Model liquidity dynamics in<br/>fractional ownership markets via ABM"] --> B["Key Inputs & Data<br/>Empirical FinTech Platform Data"]
    B --> C["Computational Process<br/>Agent-Based Model (ABM) Simulation<br/>Sell Offer-Driven System"]
    C --> D["Outcome: Data Augmentation<br/>Simulate diverse trading architectures &<br/>market structures without direct risk"]
    D --> E["Key Findings/Outcomes<br/>1. Foundation for liquidity-maximizing environment<br/>2. Framework for future market maker design"]