Performative Market Making

TL;DR

This paper mathematically formulates the concept of performativity in financial markets, showing how models influence prices and strategies, and introduces a market maker that can reverse engineer and arbitrage dominant strategies.

Contribution

It introduces a novel mathematical framework for performativity in finance by embedding models within market processes, and demonstrates a market maker that exploits this feedback loop.

Findings

Prices tend to conform to prevailing financial models.

A performative market maker can reverse engineer dominant strategies.

The approach combines closed-form solutions with machine learning techniques.

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.