AI Agents and Automation in Markets Statistics
About 37% of US equity volume ran through algorithms in 2023, and regulators at the IMF and FSB treat that figure as a systemic-risk variable, not just a market-structure stat. Each figure below names its source and year; none was generated by this site. The Coalition Greenwich data and the regulatory warnings are primary sources. Market-structure shares shift with how 'algorithmic' is defined, so the relevant figure is reported with its source's own definition.
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Statistics
The numbers worth quoting
About 37% of overall US equity volume was executed through algorithms and/or smart order routers in 2023, up from 35% the prior year
The figure reflects buy-side execution channels as measured by Coalition Greenwich in a January 2024 release. Measures of 'algorithmic' share differ across studies depending on what is counted.
Among the most active institutional traders, about 59% of flow by notional value was channeled through algorithms
Algorithmic share is markedly higher among the heaviest traders than the market-wide average, indicating automation concentrates among the most sophisticated participants.
Buy-side firms projected algorithmic trading to reach about 40% of volume within three years
The projection is the surveyed managers' own expectation of a continued upward trend, with crossing-network use also expected to rise.
The IMF warned that AI-driven trading can amplify market volatility and increase the speed and correlation of asset-price moves
The Fund flagged that wider AI adoption could deepen flash-crash dynamics and herding while also improving liquidity provision in normal conditions, a two-sided assessment.
The FSB identified market correlations from common AI models as a channel that could increase systemic risk
The FSB noted that institutions running similar AI systems could behave in a synchronized way, raising the risk of correlated moves and herding in stressed markets.
On the FinBen financial benchmark, leading LLMs performed well on extraction and analysis but struggled with forecasting and decision-making tasks such as stock trading
FinBen evaluated 15 LLMs across 24 tasks and was the first benchmark to include stock-trading evaluation, finding agentic market tasks among the hardest, which tempers expectations for autonomous trading agents.
Key Takeaways
Methodology
Figures are drawn from named research (Coalition Greenwich), regulator reports (IMF, FSB), and a peer-reviewed benchmark (FinBen), each reported with its source and year. Market-structure shares reflect the source's own definition of algorithmic execution. No statistic on this page is derived from data collected by this site.
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Paste a forecast stream (probability + outcome) and see Brier score with full decomposition, log loss, reliability diagram, and bootstrap confidence.
Sources & References
- Electronic Platforms Capture Growing Share of U.S. Equity Trading Volume — Coalition Greenwich (2024)
- Global Financial Stability Report, October 2024 — International Monetary Fund (2024)
- FinBen: A Holistic Financial Benchmark for Large Language Models — Qianqian Xie et al., NeurIPS Datasets and Benchmarks (2024)
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