Artificial Intelligence and Systemic Risk: A Unified Model of Performative Prediction, Algorithmic Herding, and Cognitive Dependency in Financial Markets
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arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915914995204096 |
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| author | Meng, Shuchen Chen, Xupeng |
| author_facet | Meng, Shuchen Chen, Xupeng |
| contents | We develop a unified model in which AI adoption in financial markets generates systemic risk through three mutually reinforcing channels: performative prediction, algorithmic herding, and cognitive dependency. Within an extended rational expectations framework with endogenous adoption, we derive an equilibrium systemic risk coupling $r(ϕ) = ϕρβ/λ'(ϕ)$, where $ϕ$ is the AI adoption share, $ρ$ the algorithmic signal correlation, $β$ the performative feedback intensity, and $λ'(ϕ)$ the endogenous effective price impact. Because $λ'(ϕ)$ is decreasing in $ϕ$, the coupling is convex in adoption, implying that the systemic risk multiplier $M = (1 - r)^{-1}$ grows superlinearly as AI penetration increases. The model is developed in three layers. First, endogenous fragility: market depth is decreasing and convex in AI adoption. Second, embedding the convex coupling within a supermodular adoption game produces a saddle-node bifurcation into an algorithmic monoculture. Third, cognitive dependency as an endogenous state variable yields an impossibility theorem (hysteresis requires dynamics beyond static frameworks) and a channel necessity theorem (each channel is individually necessary). Empirical validation uses the complete universe of SEC Form 13F filings (99.5 million holdings, 10,957 institutional managers, 2013--2024) with a Bartik shift-share instrument (first-stage $F = 22.7$). The model implies tail-loss amplification of 18--54%, economically significant relative to Basel III countercyclical buffers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03272 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence and Systemic Risk: A Unified Model of Performative Prediction, Algorithmic Herding, and Cognitive Dependency in Financial Markets Meng, Shuchen Chen, Xupeng Computational Finance Artificial Intelligence Computer Science and Game Theory General Finance We develop a unified model in which AI adoption in financial markets generates systemic risk through three mutually reinforcing channels: performative prediction, algorithmic herding, and cognitive dependency. Within an extended rational expectations framework with endogenous adoption, we derive an equilibrium systemic risk coupling $r(ϕ) = ϕρβ/λ'(ϕ)$, where $ϕ$ is the AI adoption share, $ρ$ the algorithmic signal correlation, $β$ the performative feedback intensity, and $λ'(ϕ)$ the endogenous effective price impact. Because $λ'(ϕ)$ is decreasing in $ϕ$, the coupling is convex in adoption, implying that the systemic risk multiplier $M = (1 - r)^{-1}$ grows superlinearly as AI penetration increases. The model is developed in three layers. First, endogenous fragility: market depth is decreasing and convex in AI adoption. Second, embedding the convex coupling within a supermodular adoption game produces a saddle-node bifurcation into an algorithmic monoculture. Third, cognitive dependency as an endogenous state variable yields an impossibility theorem (hysteresis requires dynamics beyond static frameworks) and a channel necessity theorem (each channel is individually necessary). Empirical validation uses the complete universe of SEC Form 13F filings (99.5 million holdings, 10,957 institutional managers, 2013--2024) with a Bartik shift-share instrument (first-stage $F = 22.7$). The model implies tail-loss amplification of 18--54%, economically significant relative to Basel III countercyclical buffers. |
| title | Artificial Intelligence and Systemic Risk: A Unified Model of Performative Prediction, Algorithmic Herding, and Cognitive Dependency in Financial Markets |
| topic | Computational Finance Artificial Intelligence Computer Science and Game Theory General Finance |
| url | https://arxiv.org/abs/2604.03272 |