Applications of AI: Financial and Capital Markets
Delivered at the Singapore FinTech Festival, this session provided an in-depth look into the potential impact of artificial intelligence (AI) on financial markets, as explored in the latest Global Financial Stability Report by the International Monetary Fund (IMF). The speaker presented key findings related to AI’s influence on capital markets, highlighting both current adoption trends and future implications for financial stability.
Key Highlights
AI Adoption in Capital Markets
- Focus on Markets, Not Banking: The IMF's analysis centred on capital market activities, distinguishing between generative and non-generative AI. The report does not delve deeply into the banking sector, keeping the focus on financial stability and market dynamics.
- Robo-Advisory Growth: The speaker noted the recent growth in robo-advisory services, although these do not always equate to true AI use. Robo-advisories represent the broader trend of automation in investment decision-making, yet AI-driven investment strategies remain nascent.
- AI-Driven ETFs: While there has been growth in AI-driven exchange-traded funds (ETFs), this market remains relatively small, with less than $1 billion in assets under management.
- Auxiliary Use of AI: Surveys reveal that most investment managers use AI for auxiliary tasks, such as incorporating alternative datasets and conducting sentiment analysis, rather than for core investment functions like asset allocation and executing decisions.
AI in Trading and Investment
- Algorithmic Trading: The adoption of machine learning in algorithmic trading is still limited, especially in execution algorithms where human oversight is preferred.
- Human in the Loop: Many market participants continue to favour human involvement in trading decisions, even when using signal-generating algorithms.
- Patent Filings and Future Growth: Evidence from patent filings indicates a growing interest in AI applications for algorithmic trading, with a notable increase in patents since 2019, driven by AI and machine learning elements.
Structural and Dynamic Impacts on Markets
AI’s Influence on Market Dynamics
- Faster Trading and Higher Volumes: AI enables the rapid processing of new information, leading to quicker and more voluminous trading. For instance, AI-driven ETFs have higher turnover ratios compared to other ETFs, whether active or passive.
- Market Structure Changes: AI adoption could lead to greater market concentration at various levels:
- Infrastructure Providers: The top three IT infrastructure providers account for over 70% of market share, highlighting the potential operational risks of relying on a few dominant firms.
- Trading Activity: AI may concentrate trading activity among a few major players, as seen in the European Stock Exchange, where the top 10 traders account for 90% of the trading volume. This has implications for market transparency and liquidity during periods of stress.
Risks and Operational Challenges
- Operational Risk from Concentration: The dominance of a few infrastructure providers could have systemic implications. For example, an incident like the recent CrowdStrike disruption could pose significant risks if many financial institutions rely on the same providers.
- Liquidity and Market Opacity: The rise of algorithmic trading has shifted activity to non-bank financial intermediaries, making markets more opaque and difficult to monitor. These algorithms provide liquidity under normal conditions but may withdraw quickly in times of market stress, exacerbating liquidity issues.
Potential Threats from AI Misuse
- Cyber Attacks and Market Manipulation: The finance and insurance sectors have seen a rise in cyber attacks. Additionally, AI can be exploited for market manipulation, as demonstrated by the May 2023 incident involving a fake tweet about the Pentagon exploding, which briefly affected US equity markets.
- Social Media Risks: AI-driven algorithms that monitor social media activity could amplify the impact of false information on financial markets.
Conclusions and Policy Recommendations
While AI holds the promise of making markets faster and more efficient, there are significant challenges related to concentration, liquidity, and the potential misuse of technology. Policymakers and financial institutions must work collaboratively to ensure that AI's benefits are realised while minimising systemic risks.
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Speaker:
- Dr. Benjamin Mosk, Senior Financial Sector Expert, Global Markets Analysis Division, Monetary & Capital Markets Department (MCM-GA), International Monetary Fund