Artificial Intelligence Can Make Markets More Efficient—and More Volatile
IMF Blog, October 15, 2024
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Bibliographic details
- Authors: Nassira Abbas, Charles Cohen, Dirk Jan Grolleman, Benjamin Mosk
- Published: October 15, 2024
Key findings on AI adoption in capital markets
- AI-driven trading could lead to faster and more efficient markets, but also higher trading volumes and greater volatility in times of stress.
- Since large language models (LLMs) started to appear in 2017, the share of AI content in patent applications related to algorithmic trading has risen from 19 percent in 2017 to over 50 percent each year since 2020.
- AI's ability to quickly rebalance investment portfolios is likely to lead to higher trading volumes; market participants surveyed expect high-frequency, AI-driven trading to become more prevalent, particularly in liquid asset classes like equities, government bonds, and listed derivatives.
- Market participants foresee greater integration of sophisticated AI in investment and trading decisions within three to five years, although a “human in the loop” approach is expected to persist, especially for large capital allocation decisions.
- Evidence in the exchange-traded fund (ETF) market: AI-driven ETFs show a significantly higher turnover compared to other ETFs. While a typical actively managed equity ETF turns over its holdings much less than once a year, AI-driven ETFs do so about once a month.
- Several AI-driven ETFs saw increased turnover during the March 2020 market turmoil, indicating potential for increased herd-like selling during times of stress.
- Prices may react much more quickly in an AI-driven market: since 2017 and the introduction of LLMs, the movement of US equity prices 15 seconds after the release of the Fed minutes seem to be more consistently in the direction of the longer-lasting movement seen after 15 minutes, in contrast to the apparently uncorrelated movements in the pre-LLM period.
- Nonbank financial intermediaries (hedge funds, proprietary trading firms, and others) may gain a further structural advantage in adopting AI because they are generally more agile and subject to fewer regulatory constraints than large commercial and investment banks.
Risks and market-structure implications
- Potential benefits:
- Improved risk management.
- Deeper liquidity and faster price discovery in normal times.
- Potential risks:
- Greater opacity and reduced market transparency if investment migrates to nonbanks.
- Increased vulnerability to cyber-attacks and manipulation risks.
- Potential for “flash crash” events or amplified selling in times of stress due to rapid, correlated AI-driven decision-making.
- Harder-to-monitor markets as nonbanks rise in importance and rely on complex AI systems with legacy-agnostic deployment.
Evidence and indicators to watch
- Patent filing trends showing AI content rising from 19 percent in 2017 to over 50 percent each year since 2020.
- ETF turnover patterns: typical actively managed equity ETFs turn over holdings much less than once a year; AI-driven ETFs turn over about once a month.
- Observed behavior around policy communication: more consistent price movements at 15 seconds after Fed minutes since 2017/LLMs compared with the pre-LLM period.
- Historical stress episodes: increased turnover in some AI-driven ETFs during March 2020 market turmoil.
Policy recommendations for regulators and supervisors
- Design or modify volatility response mechanisms to address potential AI-originated “flash crash” events; consider the role of:
- Margin requirements.
- Circuit breakers.
- Resilience of central counterparties (CCPs).
- Strengthen oversight and regulation of nonbank financial intermediaries by:
- Requiring them to identify themselves and disclose AI-relevant information.
- Requiring financial institutions to regularly map interdependencies between data, models, and the technological infrastructure supporting AI models.
- Monitor and oversee rapidly changing market conditions closely to enable a balanced regulatory response that allows participants to benefit from AI while mitigating associated risks.
—This blog is based on Chapter 3 of the October 2024 Global Financial Stability Report, “Advances in Artificial Intelligence: Implications for Capital Market Activities.”