Performative Market Making

2025-08-06

AI Summary

This paper addresses the lack of rigorous mathematical formalization of *performativity* in financial models—the phenomenon wherein models actively shape market behavior through self-fulfilling prophecies. We propose the first formal closed-loop feedback modeling framework that integrates stochastic diffusion processes, closed-form analytical solutions, and machine learning to endogenously couple market dynamics with model predictions. Our method enables dynamic capture of price-convergence mechanisms and backward derivation of optimal trading strategies. Key contributions include: (i) the first mathematically precise formulation of performativity theory; and (ii) a deployable embedded market-making mechanism that enhances P&L significantly while preserving liquidity. Empirical evaluation demonstrates both theoretical coherence and practical efficacy, thereby bridging the fundamental gap between financial modeling and real-world market evolution.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Machine Learning: Probabilistic Circuits and Graphical Models
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Financial models do not merely analyse markets, but actively shape them. This effect, known as performativity, describes how financial theories and the subsequent actions based on them influence market processes, by creating self-fulfilling prophecies. Although discussed in the literature on economic sociology, this deeply rooted phenomenon lacks mathematical formulation in financial markets. Our paper closes this gap by breaking down the canonical separation of diffusion processes between the description of the market environment and the financial model. We do that by embedding the model in the process itself, creating a closed feedback loop, and demonstrate how prices change towards greater conformity to the prevailing financial model used in the market. We further show, with closed-form solutions and machine learning, how a performative market maker can reverse engineer the current dominant strategies in the market and effectively arbitrage them while maintaining competitive quotes and superior P&L.

Problem

• Mathematically modeling performativity in financial markets
• Embedding financial models to create feedback loops
• Arbitraging dominant strategies via performative market making

Innovation

• Embedding model in market process
• Creating closed feedback loop
• Using machine learning for arbitrage