Reproduce “AI-Driven Alpha Decay: Algorithmic Homogenization, Reflexive Signal Erosion, and the Paradox of Intelligent Markets” on OpenResearch
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About this paper
Shuchen Meng, Xupeng Chen
Meng and Chen present a theoretical model demonstrating how widespread AI adoption in asset management leads to the decay of excess returns, introducing mechanisms like performative signal erosion and Red Queen dynamics. Their research indicates that AI-driven markets, while individually accurate, collectively foster overinvestment in AI, reduce signal half-lives from years to months, and increase systemic fragility through an efficiency-fragility tradeoff.
Key insights
- The Alpha Half-Life Theorem demonstrates that the lifespan of exploitable trading signals is convex-decreasing with AI adoption, shortening from an estimated 5-7 years in the pre-AI era to approximately 18 months at current adoption levels.
- A Signal Extinction Cascade shows that beyond a critical AI adoption threshold, performative erosion and crowding lead to a cascading decay, where the extinction of one signal accelerates competition and decay for others.
- The Red Queen Impossibility indicates that in a competitive AI-driven market, aggregate net alpha from AI investment converges to zero, leading to overinvestment in AI resources without generating superior returns.
- An Efficiency-Fragility Tradeoff reveals that the level of AI adoption maximizing price discovery is higher than the level minimizing systemic fragility, implying that market equilibrium overshoots socially optimal adoption levels.
Summary by alphaXiv.
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