Published on: 2025-12-09
AI is no longer just a buzzword on trading floors. Quant desks, hedge funds, robo-advisers and retail platforms are wiring machine learning into everything from signal generation to execution and risk control.
In parallel, investors are literally asking chatbots which stocks to buy, and robo-advisers using AI already manage more than $1 trillion in client assets.
That mix of institutional firepower and retail curiosity has created a clear shift: AI trading is now a core part of how markets work, not a side project.
At its core, AI trading means using models that learn from data, often machine learning or deep learning, to help make trading and investment decisions.
Rather than relying solely on preset rules ("purchase if RSI < 30"), AI models analyse extensive data sets, identify patterns, and generate signals, position sizes, or execution decisions.
It's a component of a larger trend. For instance, the Stanford AI Index indicates that private AI investment in the US reached approximately $109.1 billion in 2024, with around 78% of organisations worldwide stating they utilised AI in 2024, an increase from 55% in 2023
It helps to separate traditional algorithmic trading from AI-driven trading:
Classic algos follow fixed, human-designed rules (VWAP/TWAP, simple stat-arb, if-then logic).
AI algos learn those rules from data and keep adjusting as new data arrives.
Both are automated. The difference is that AI can pick up patterns humans didn't explicitly specify, but that flexibility comes with extra model risk and opacity.
AI trading lives or dies on data quality. Typical inputs include:
Market data: Prices, volumes, order-book depth, implied vols.
Fundamental & macro: Earnings, balance sheets, macro releases.
Alternative data: News, social media, satellite images, credit-card data, shipping flows.
Textual data: Corporate documents, financial conference calls, and central bank addresses analysed using natural language processing (NLP)
Quants then build features: transformations of raw data (returns, spreads, volatility, sentiment scores, anomalies) that models can learn from.
Common AI approaches in trading include:
Supervised learning: Predicting returns, volatility or probabilities (e.g., next-day up/down).
Unsupervised learning: Clustering regimes, grouping similar stocks, detecting anomalies.
Reinforcement learning: Learning a trading or execution policy by "rewarding" good outcomes in simulated markets.
Models are backtested across multiple regimes with strict validation to reduce overfitting, a major concern repeatedly flagged by regulators and researchers, given how noisy market data is.
Once a model passes testing, it's deployed into live systems that:
Convert signals into orders and position sizes.
Route orders via smart execution algos, often also AI-tuned.
Monitor slippage, risk limits, and model drift in real time.
Recommended by IOSCO, ESMA, and the OECD as fundamental standards, Major companies combine AI with comprehensive governance, model documentation, human approval, kill-switches, and regular assessments.
| Approach | Typical holding period | Main data | Common AI techniques | Typical users |
|---|---|---|---|---|
| Short-term signal models | Seconds to days | Tick data, order book, news headlines | Gradient boosting, deep nets, NLP | Quant hedge funds, prop shops |
| Execution & smart order routing | Minutes to hours | Order book, venue micro-structure | Reinforcement learning, bandit algorithms | Banks, HFT firms, large asset managers |
| Market-making & liquidity provision | Milliseconds to minutes | Quotes, inventory, volatility | Reinforcement learning, probabilistic models | HFT firms, crypto market-makers |
| Cross-asset & macro allocation | Days to months | Macro data, fundamentals, sentiment | Supervised learning, regime clustering | Multi-asset funds, sovereigns |
| Options & volatility strategies | Hours to weeks | Implied vols, realised vols, skew, flow | Non-linear regression, neural nets | Vol desks, structured-product desks |
| Robo-advisers & portfolios | Months to years | Client data, ETF prices, risk metrics | Portfolio optimisation with ML overlays | Retail platforms, wealth managers |
Robo-advisers in particular have become a visible face of AI in markets. As of 2025, automated platforms using algorithms and machine learning to manage portfolios oversee more than $1 trillion in assets, typically via diversified ETF portfolios with automated rebalancing and tax-loss harvesting.
Hedge funds were early adopters of machine learning. A 2025 IG Prime survey reported that around 86% of hedge fund managers now use generative AI tools in their work, mainly for research, data processing and content generation, even when their core trading strategies are not purely AI-driven.
The European Securities and Markets Authority (ESMA) has recently reported an increasing number of EU investment funds employing AI and NLP for investment strategies, risk management, and compliance. However, uptake remains primarily among larger companies.
Banks and brokers utilise AI in both front-end and back-end operations. International organisations such as IOSCO and the OECD observe that AI is now integrated into trading, robo-advisory services, credit, insurance, and operational risk management.
On the retail side, AI is showing up in:
Robo-advisers
AI-themed ETFs
Chatbots and screeners
A Reuters survey in 2025 found that about one in ten retail investors already use AI tools to select stocks, with the robo-advisory market projected to grow from $61.75 billion in 2024 to $470.91 billion by 2029.
| Feature | Human discretionary trader | Rules-based quant | AI-driven quant |
|---|---|---|---|
| Decision logic | Experience, intuition, narratives | Fixed formulas & rules | Learned from data; adapts over time |
| Speed & scale | Limited by attention | High | Very high, across many assets/data sets |
| Data used | Charts, news, some fundamentals | Market & fundamental data | Market, fundamentals, alt-data, text, sometimes images |
| Transparency | High. You can ask "why?" | High. Rules documented | Often lower – complex models can be opaque |
| Typical users | Discretionary desks, private traders | Many funds, banks, HFT | Quant funds, banks, robo-advisers, larger platforms |
| Strengths | Flexibility, context, macro sensemaking | Discipline, backtestable, scalable | Pattern detection, automation, personalisation |
| Weaknesses | Biases, fatigue, emotions | Can be rigid, easier to copy | Model risk, data bias, governance & explainability costs |
In practice, the most robust desks increasingly blend all three: human macro judgement, rules-based risk limits and AI tools where they truly add value.
Machines can monitor thousands of instruments and alternative data streams in real time.
Models spot non-linear relationships that simple factor models might miss.
Vital for crypto and global futures.
AI systems don't panic, chase FOMO or get bored. The risk discipline is only as good as the code, but it's consistent.
In wealth management, AI allows robo-advisers to tailor portfolios to goals, risk scores and tax situations at low cost.
AI models learn from history. When the regime changes, such as new policies, wars, or pandemics, yesterday's patterns can break.
OECD and IOSCO reports highlight
Overfitting
Data bias and leakage
Lack of explainability
If many funds use similar signals and models, trades can become crowded. When the trade goes wrong, everyone rushes for the exit, amplifying volatility.
Regulators are very focused on AI in finance:
The SEC has initiated enforcement actions for "AI-washing."
IOSCO's 2025 consultation on AI in capital markets stresses governance, testing, human oversight and clear accountability.
For traders and investors, that boils down to a simple warning: don't believe any product just because it stamps "AI" on the label. Check what it actually does.
The honest answer so far: Sometimes, in specific conditions, but not reliably across the board.
The Eurekahedge AI Hedge Fund Index delivered about 9.8% annualised from December 2009 to July 2024, versus 13.7% for the S&P 500 over the same period.
Earlier work showed the same index returning roughly 115% from 2011 to 2020, versus 210% for the S&P 500 and 133% for MSCI World, again lagging simple equity benchmarks.
In short, AI is a powerful tool, not a magic alpha machine. It can help with specific tasks, especially pattern detection and risk control. But, it still faces the same fees, transaction costs and market noise as any other active strategy.
Not always. Many HFT firms use deterministic, rule-based strategies that rely on speed and co-location rather than AI.
Not necessarily. Most retail access comes via robo-advisers, AI-enhanced broker tools and analytics platforms with user-friendly interfaces.
Treat AI as decision support, not a substitute for understanding. Use it to process information, highlight risks and suggest ideas, but keep human control over goals, risk tolerance and final decisions.
In conclusion, AI trading has transitioned from being a trial to a common occurrence in markets. Hedge funds, asset managers, banks, and retail platforms increasingly rely on machine learning for research, execution, and portfolio development.
The edge today lies in using AI where it truly adds value. Treat it as another tool in the kit, under solid governance, with your risk limits and not the algorithm in charge.
Disclaimer: This material is for general information purposes only and is not intended as (and should not be considered to be) financial, investment or other advice on which reliance should be placed. No opinion given in the material constitutes a recommendation by EBC or the author that any particular investment, security, transaction or investment strategy is suitable for any specific person.
