# The Invisible Handshake: Persistent Overpricing by Adaptive Market Agents

arXiv: https://arxiv.org/abs/2510.15995 · HTML: https://mlsanity.com/paper/2510.15995/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [Market Making via Reinforcement Learning](https://mlsanity.com/paper/1804.04216/) (2018)
- [Reinforcement Learning for Market Making in a Multi-agent Dealer Market](https://mlsanity.com/paper/1911.05892/) (2019)
- [Learning to simulate realistic limit order book markets from data as a World Agent](https://mlsanity.com/paper/2210.09897/) (2022)
- [Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States](https://mlsanity.com/paper/2202.02765/) (2022)
- [Model-based Reinforcement Learning for Predictions and Control for Limit Order Books](https://mlsanity.com/paper/1910.03743/) (2019)
- [Robust Market Making via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2003.01820/) (2020)
- [Efficient and Near-Optimal Online Portfolio Selection](https://mlsanity.com/paper/2209.13932/) (2022)
- [Uniswap Liquidity Provision: An Online Learning Approach](https://mlsanity.com/paper/2302.00610/) (2023)
- [Market Making without Regret](https://mlsanity.com/paper/2411.13993/) (2024)
- [Learning Unfair Trading: a Market Manipulation Analysis From the Reinforcement Learning Perspective](https://mlsanity.com/paper/1511.00740/) (2015)

## Abstract (arXiv)

We study overpricing in a repeated game between two representative agents: a market maker, who controls market liquidity, and a market taker, who chooses trade quantities. Market prices evolve through the endogenous price impact of trades and exogenous shocks. We define overpricing relative to a counterfactual price path that holds fixed the same sequence of shocks while shutting down price impact, and characterize the set of feasible strategy profiles that generate persistent overpricing while respecting cash and inventory constraints. We provide a sufficient condition for decentralized learning to reach the overpricing region in finite time, and we show that this condition is satisfied, in particular, by projected stochastic gradient ascent. A key step in the analysis is a decomposition of the game into a competitive component, which favors zero price impact, and a collaborative component, which makes overpricing jointly profitable when aggregate inventory is positive. We further show that the same structural incentives govern both myopic and farsighted objectives. Together, these results show how decentralized learning by adaptive market agents can lead to persistent overpricing in financial markets.