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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.15995 |
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| _version_ | 1866911671356751872 |
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| author | Foscari, Luigi Guidotti, Emanuele Cesa-Bianchi, Nicolò Chavdarova, Tatjana Ferrara, Alfio |
| author_facet | Foscari, Luigi Guidotti, Emanuele Cesa-Bianchi, Nicolò Chavdarova, Tatjana Ferrara, Alfio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15995 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Invisible Handshake: Persistent Overpricing by Adaptive Market Agents Foscari, Luigi Guidotti, Emanuele Cesa-Bianchi, Nicolò Chavdarova, Tatjana Ferrara, Alfio Trading and Market Microstructure Computer Science and Game Theory Machine Learning 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. |
| title | The Invisible Handshake: Persistent Overpricing by Adaptive Market Agents |
| topic | Trading and Market Microstructure Computer Science and Game Theory Machine Learning |
| url | https://arxiv.org/abs/2510.15995 |