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What “AI trading” can mean
AI in financial markets is not one product or strategy. It can refer to machine-learning models that analyze data, generative AI used in research or operations, or systems that help route and execute orders. A model that explains or predicts a market pattern may be useful without producing a profitable portfolio; profitability depends on how the signal is turned into trades and what those trades cost.
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FINRA’s AI Applications in the Securities Industry describes use cases at securities firms, not evidence that those applications outperform. The distinction matters: adoption shows that firms are exploring a tool, not that it delivers better returns for investors.
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Portfolio management and market research
FINRA describes machine learning for identifying patterns and predicting potential price movements. Models may draw on conventional financial data as well as nontraditional sources such as social media or satellite imagery. Such systems can help analysts or portfolio teams process information, but a pattern found in data is not automatically a reliable forecast or a tradeable signal.
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Trading and execution
Trading applications include machine learning for smart order routing, price optimization, best execution and allocating large block trades. These tasks concern how orders are handled as well as whether a portfolio should hold a particular asset. Better execution can matter to trading outcomes, but it does not by itself establish that a model can predict market direction.
Generative AI and operations
Firms may also use generative AI in business processes. FINRA’s Regulatory Notice 24-09, issued June 27, 2024, says existing rules and securities laws continue to apply when member firms use generative AI or similar technology. The notice does not create a separate exemption from firms’ existing obligations.
What the studies say about AI and investment performance
Two 2026 National Bureau of Economic Research (NBER) working papers report different kinds of results. One studies hedge-fund performance over time; the other evaluates how well agentic systems explain stock returns around earnings announcements. Neither result is a universal estimate of what an investor can earn from AI trading.
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| Study | Reported result | What it measures—and what it does not |
|---|---|---|
| Shuang Chen, Clemens Sialm and David X. Xu, NBER Working Paper 35273, May 2026 | AI hedge funds outperformed non-AI hedge funds in the early years studied, but that outperformance declined over time, including among early adopters. | A qualitative finding about historical relative performance in the funds studied. The abstract does not provide a numeric return estimate; it does not establish future performance or a durable edge. |
| Ralph S. J. Koijen and Bradford Levy, NBER Working Paper 35431, July 2026 | For the paper’s best-optimized agentic systems, R² rose from 8% to close to 20% in a real-time earnings-announcement asset-pricing benchmark. | Explained variation in contemporaneous stock returns around announcements under that benchmark. It is not a 12-point increase in investor returns, a portfolio return, or proof of alpha after costs. |
The results answer different questions. Relative hedge-fund performance is not the same measure as a benchmark’s explained variation. Neither supplies a general win rate, average return or success rate for retail AI traders; the reviewed material establishes no universal, independently verified figure for AI trading’s net returns.
Why a promising backtest may not survive live trading
Look-ahead bias and repeated strategy searches
A backtest can appear predictive if it accidentally uses information that would not have been available at the time of a trade. The NBER benchmark paper highlights look-ahead bias as a concern. Researchers and investors also need to account for how many strategies, features and parameter choices were tried: selecting the best result from many experiments can make chance look like skill.
Costs, liquidity and execution
A signal can look profitable before accounting for transaction costs, bid-ask spreads, slippage, liquidity and the market impact of placing an order. A credible evaluation should model these frictions realistically. A model’s ability to identify a pattern or explain returns is not enough if the corresponding trades cannot be executed at the assumed prices.
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Changing markets and model failure
Models learn from historical data, but future conditions may differ. FINRA warns that circumstances not captured in training—such as unusual volatility, natural disasters, pandemics or geopolitical changes—can make predictions unreliable and prompt undesirable trading behavior. Performance should therefore be evaluated across different market regimes, not just the period that made a strategy look attractive.
Reflexivity and crowding
Markets respond to participants. If many firms adopt similar strategies, their trades can alter the patterns those models seek to exploit, weakening an apparent edge. FINRA also relays concerns that models learning from one another could contribute to herd behavior or unpredictable outcomes. Separately, the 2025 NBER Working Paper 34054 by Winston Wei Dou, Itay Goldstein and Yan Ji analyzes AI-powered trading and collusion theoretically and through simulations; that kind of analysis raises a mechanism to examine, not proof that real-world AI systems are colluding.
How to evaluate a claimed AI market edge
Before treating a model’s performance as evidence of an investable strategy, examine the full chain from data to live trading. A useful assessment asks:
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- Was the test genuinely out of sample? Check that the model was evaluated on data not used to build, tune or select it.
- Could future information have leaked into the past? Review timestamps for features, labels and trade decisions to rule out look-ahead bias.
- How many alternatives were tested? A winning result has less evidential weight if it was selected after trying many strategies or parameter settings.
- Is there a testable economic explanation? A plausible reason for a signal to exist is more informative than a pattern alone. Marcos M. López de Prado writes in the Cambridge University Press excerpt for Machine Learning for Asset Managers: “Without a testable theory that explains your edge, the odds are that you do not have an edge at all.”
- Are trading frictions included? Check the assumptions for costs, liquidity, slippage and execution, and whether they resemble the conditions in which the strategy would actually trade.
- Does performance hold across assets and regimes? Results limited to one asset, market period or unusual event may not generalize.
- Is the system monitored after launch? A model needs review when a strategy is introduced or changed and after it begins operating, not just a one-time backtest.
These checks do not guarantee that a strategy will work. They help separate evidence about a model’s historical or benchmark performance from evidence that an investor could capture returns in practice.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What controls firms should apply to algorithmic trading
FINRA’s Algorithmic Trading guidance identifies controls across the life of a strategy. These include development and implementation practices, testing before production, system validation, review after a strategy is introduced or changed, and effective communication between compliance and the relevant business and technology teams. FINRA’s guidance on generative AI likewise makes clear that existing requirements remain applicable to member firms using the technology.
For readers, the practical point is that “AI-powered” does not remove the need for oversight. A firm’s model should be assessed as part of its trading and compliance process, including how it behaves when conditions change—not just by a headline performance figure.
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How retail investors can assess auto-trading services
An institutional model used for research or order execution is different from a third-party service that sends trade instructions directly to a person’s brokerage account. FINRA’s July 29, 2025 investor alert, Know the Risks of Auto-Trading Services Offered by Unregistered Entities, warns about unregistered promoters making claims about AI, risk-free trading or high monthly returns. It describes some promoters claiming “consistent monthly returns of more than 10 percent”; that is a claim the alert warns readers to scrutinize, not a verified performance statistic.
Be especially cautious if a service:
- promises consistent, high or risk-free returns, or relies on AI branding instead of independently verifiable results;
- is offered by an entity whose registration and background you have not checked; or
- asks you to hand over brokerage login credentials, creating privacy and financial-safety risks.
Do not treat a projected return, backtest screenshot or promotional claim as proof of results. Verify who controls the account and how trades are authorized before connecting any service to a brokerage account.
Can AI give an investor an edge?
AI can support financial analysis and trading, and the cited studies report qualified findings in particular settings. But usefulness, predictive power and investable outperformance are separate claims. A durable edge would need to withstand careful testing, trading frictions, changing market conditions and competition from other traders. The evidence here supports neither a guarantee that AI can beat the market nor a blanket claim that it cannot; it calls for scrutiny of the specific model, strategy and evidence behind each claim.
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