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How machine learning is used in trading
Machine-learning systems use historical and current data to estimate patterns and produce signals or recommendations. Depending on the application, they may help with:
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| Use | What the system may do |
|---|---|
| Signal generation | Analyze market data and, in some applications, unstructured inputs such as text to identify patterns that may inform a trading decision. |
| Execution | Help select when or where to place an order. |
| Portfolio decisions | Support decisions about positions using historical patterns and current conditions. |
| Risk management | Help assess exposures or flag potential risks for review. |
| Surveillance | Analyze trading activity to help identify potential market abuse or suspicious behavior. |
These applications generally extend established quantitative and algorithmic methods rather than replace them wholesale. The Federal Reserve’s November 2025 Financial Stability Report says most AI applications in trading appear to build on existing machine-learning and data-analysis practices. More complex models can incorporate more information, but complexity may come at the cost of speed, so the appropriate approach depends on the task.
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Potentially richer analysis and price discovery
Models that draw on different data or interpret information differently may produce a broader range of trading responses. In principle, that diversity can help information reach prices and support price discovery. It is a possible mechanism, not proof that any particular model improves market quality.
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Potentially more similar—and faster—responses
If firms rely on similar data, models, objectives or providers, their systems may respond to the same signal in similar ways. That could reinforce price moves, contribute to crowded positions or amplify a sudden dislocation. The Federal Reserve identifies these as risks worth monitoring, while noting that historical evidence on algorithmic trading does not establish that correlated trading has necessarily harmed market quality.
The label “AI” alone does not determine the outcome. Data choices, model architecture, trading objectives, deployment speed and interactions among firms all matter. The available official materials do not establish a universal causal effect of machine learning on live-market returns, liquidity, volatility or price discovery.
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Why model architecture matters—and what a simulation shows
An ECB Research Bulletin published on 21 May 2026 describes simulation experiments in a stylized mutual-fund redemption game. In the model, Q-learning investors coordinated and redeemed even when fundamentals were strong. LLM agents did not redeem in that strong-fundamentals condition, but their behavior was less predictable when fundamentals were intermediate.
This is a bounded result about different architectures under the simulation’s assumptions. It is not evidence that deployed trading systems have caused bank runs or that one architecture is safer in live markets. The bulletin also cites a 60–70% estimate for machine-learning-based algorithmic trading’s share of equity transaction volumes in the United States and other major global markets, attributing it to Foucault et al. (2025). That is a secondary attribution in the bulletin; it should not be generalized to all markets or all forms of AI.
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Does AI trading work, and can a bot guarantee returns?
Machine learning can expand analytical and operational capabilities, but the cited official materials do not establish a general realized-return advantage across assets and market conditions, especially after costs. A model that predicts a pattern accurately in one dataset may not produce a profitable strategy once it is deployed, faces changing conditions or incurs trading expenses.
The CFTC Office of Customer Education and Outreach puts the limitation plainly: “AI technology can’t predict the future or sudden market changes.” Claims of guaranteed or extraordinary returns from a trading bot or signal service are therefore a warning sign, not evidence of reliable performance. Fees, spreads, subscriptions and the risks of the underlying assets also affect outcomes.
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Risks to assess before relying on a system
- Overfitting and regime change: A model may learn noise or relationships that do not persist. Historical success does not ensure performance when market conditions change.
- Data quality, poisoning and privacy: Inaccurate or manipulated inputs can undermine outputs; mishandling sensitive information can expose customers or firms.
- Opacity and accountability: Complex decisions can be harder to explain, audit and govern. A firm still needs to understand and take responsibility for how its system acts.
- Correlated trading and instability: Shared signals or similar reactions may make trading more alike and could intensify volatility or rapid dislocations.
- Manipulation and market conduct: Self-learning behavior may make suspicious activity more complex to detect. Machine learning can also assist surveillance, so its effect depends on how it is used.
- Concentration and provider dependence: Costly models or reliance on a small number of providers can concentrate capability and create operational dependencies.
- Cybersecurity and operational failure: Attacks, outages or erroneous outputs can have consequences, particularly when systems act quickly.
How to evaluate a trading system or its adoption
Prediction accuracy alone does not show that a strategy is suitable or profitable. A sound assessment should examine the model’s evidence, costs, controls and likely interactions with other market participants.
- Identify the use case. Establish whether the system generates signals, guides execution, informs portfolio choices, supports risk management or performs surveillance. The task determines what performance and safeguards matter.
- Examine the model and data. Ask what architecture is used, where training and validation data came from, which market regimes they cover, and how the system is protected against poisoning or data leakage.
- Review performance evidence. Look for out-of-sample and regime-specific results, risk-adjusted performance, drawdowns and transaction costs. Treat simulation results and vendor claims as different from evidence of live performance.
- Check operational controls. Assess human oversight, pre-trade limits, kill switches, change management, incident response, cybersecurity and resilience.
- Establish explainability and ownership. Determine whether the organization can explain, test and monitor decisions—and who is accountable for their effects.
- Consider market interaction. Assess whether strategies may be crowded or respond similarly to others, how they may behave when liquidity is limited, and what safeguards apply.
What regulators emphasize
The Financial Conduct Authority’s 21 August 2025 review examined algorithmic-trading controls at a sample of 10 principal trading firms. It emphasized governance, development and testing, risk controls, and market-abuse surveillance; the sample size is not an estimate of industry-wide failure rates. The FCA described the review as observations on existing controls and supervisory expectations, not as creating new requirements. Its legal relevance depends on jurisdiction and the rules applicable to a firm.
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The FCA stated: “It is essential that firms’ controls and key oversight functions, including compliance and risk management, keep pace with the ever-increasing complexity and speed of financial markets and technological advancements.” The CFTC Technology Advisory Committee has likewise identified responsible-AI considerations including transparency, explainability and robustness. Together, these themes point to a practical requirement: controls and oversight must be designed for the model’s actual use, speed and potential impact.
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