Algorithmic Exuberance
A new study reveals that financial markets have grown more volatile and unpredictable due to algorithms interacting with each other and amplifying news, not just economic events. The author introduces 'Algorithmic Exuberance' and the Reflexivity Index, which tracks volatility caused by these feedback loops. Data from 1980 to 2024 shows a sharp rise in extreme price swings since the early 2000s, linked to automation and AI. Events like the 2010 Flash Crash highlight these risks.
What it examines
This paper introduces the concept of Algorithmic Exuberance, showing how financial market volatility can arise from feedback between trading algorithms and information systems. Using market data, it develops a Reflexivity Index to measure this effect, aiming to explain persistent volatility in modern, automated markets.
What it concludes
The study finds that much of today's market volatility is self-generated by algorithmic feedback, not just new information. This insight can help design safer trading systems and improve risk management. Future research may focus on controlling feedback to keep markets stable as automation and AI continue to grow.
Evidence objects
Modern financial markets are increasingly volatile and unpredictable, not due to new economic news, but because of feedback loops between trading algorithms and information systems, a phenomenon termed 'Algorithmic Exuberance.'.
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The study introduces the Reflexivity Index, a novel metric quantifying how much market volatility stems from algorithmic feedback rather than real-world events, using innovative statistical methods to separate reflexive from fundamental-driven changes.
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Data from 1980 to 2024 reveal a sharp rise in persistent volatility, extreme price swings, and higher reflexivity since the early 2000s, with events like the 2010 Flash Crash and 2021 GameStop surge as key examples.
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This paper introduces 'Algorithmic Exuberance' and a dual-channel reflexivity mechanism in financial markets, proposing the novel Reflexivity Index (RI) with empirical measures RSV and IR. Its operational quantification of endogenous volatility from algorithmic feedback is original, offering fresh insights into AI-driven market dynamics and structural volatility shifts, making it compelling.
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Raw abstract and provenance
- … A large literature on algorithmic trading and market microstructure studies how automation reshapes liquidity and volatility. In the foreign exchange market, Chaboud et al. (…
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