Automated arbitrageurs can and often do exacerbate short-term volatility on thinly traded tokens, particularly where liquidity is shallow and markets are fragmented. Empirical and theoretical literature shows algorithmic traders improve quoted liquidity under normal conditions but retract rapidly when risk or price divergence rises, leaving thin venues exposed.
Causes and market mechanics
Market-making models by Marco Avellaneda at New York University Courant Institute and Sasha Stoikov at New York University explain how quantitative liquidity providers balance inventory risk against expected profits. In thin markets inventory imbalances from a single large trade force algorithms to adjust quotes aggressively, widening spreads and moving prices. Terrence Hendershott at University of California, Berkeley, Charles M. Jones at Columbia Business School, and Albert J. Menkveld at Vrije Universiteit Amsterdam document that algorithmic trading generally improves liquidity but can withdraw it during volatility, amplifying price moves on low-volume listings.
Feedback loops and liquidity spirals
The liquidity spiral framework developed by Markus K. Brunnermeier at Princeton University and Lasse Heje Pedersen at New York University describes how deleveraging and forced exits reduce market depth, producing transient but sharp price swings. On thinly traded tokens, automated arbitrageurs chasing cross-exchange price differences can create rapid cascades: one bot’s trade moves the local price, other bots detect divergence and trade, driving further mechanical re-pricing. These dynamics are particularly acute when order books are shallow and latency differences are small relative to trade size.
Consequences include increased realized short-term volatility, episodic flash crashes, and reputational harm for local exchanges or token projects. Retail traders in regional or smaller exchanges are disproportionately affected because they face wider effective spreads and greater slippage. Territorial fragmentation of trading venues across jurisdictions can increase arbitrage opportunity and thus transient volatility when automated systems move capital between venues.
Policymakers and platform operators can mitigate harms through minimum liquidity commitments, tighter market surveillance, and coordinated circuit breakers. Understanding comes from cross-disciplinary research blending market-microstructure theory and empirical study by recognized authorities, which highlights that algorithmic arbitrage is not inherently destabilizing but can magnify short-term volatility in fragile, thin markets. Context matters: token economics, exchange rules, and local trader behavior all shape whether arbitrageurs calm or convulse prices.