FRM Exam Part II · Advances in Artificial Intelligence: Implications for Capital Markets Activities

AI Use Cases in Trading and Market Making

Q: How does AI improve trade execution?

It uses live data to decide order size, timing, venue and order type, which can reduce market impact and spread cost compared with fixed schedules. It cannot remove the trade-off between impact and timing risk.

Q: How does machine learning help market making?

It adjusts quotes and spreads to volatility, order flow and inventory, and estimates the chance a trade is informed. This helps manage inventory risk and adverse selection.

Q: What are the main risks of AI in trading?

The main risks are overfitting, regime change, model opacity, herding among similar models, and concentration in data or cloud providers. In stress, automated liquidity providers may withdraw.

Q: Does the FRM exam ask for calculations on this topic?

Mostly it is conceptual, with scenario questions. Simple calculations such as spread, effective spread or implementation shortfall can appear.

Updated 11 October 2026 · Fact-checked

AI in trading uses machine learning to forecast prices, choose how to split and time orders, and set quotes in market making. It can cut execution cost and improve liquidity provision. It also adds model risk, herding and fragility in stress. Exam questions ask you to match the use case to its benefit and risk.

Understand AI Use Cases in Trading and Market Making

Start with the trading chain. A firm decides what to trade, then how to execute it, then how to quote prices if it is a market maker. AI can sit in each link. Traditional algorithms follow fixed rules written by people. Machine learning models learn patterns from data and can update the rules themselves.

Price prediction uses models such as regularised regressions, tree ensembles and neural networks on market data, order book data and sometimes alternative data like news text. Signals are weak and noisy. Models are prone to overfitting: they fit past noise and fail out of sample. Financial data also changes regime, so a model trained in calm markets can break in stress.

Execution is about reducing cost when you trade a large order. Costs are the bid-ask spread, market impact and timing risk. Trading too fast raises impact. Trading too slowly raises price risk. Reinforcement learning and predictive models help choose order size, timing, venue and order type (limit or market) using live conditions, instead of a fixed schedule such as TWAP or VWAP.

Market making means quoting both a bid and an ask and earning the spread. The risks are inventory risk (holding positions as prices move) and adverse selection (trading against better informed counterparties). AI helps by adjusting quotes and spreads to volatility, order flow and inventory, and by estimating the chance a trade is informed. This can tighten spreads and add liquidity in normal times.

The risk side matters as much for the exam. Many firms using similar models and data can trade in the same direction, which causes herding and amplifies price moves. Models are hard to explain, so validation and model risk controls are harder. Market makers may withdraw quotes when their models see stress, so liquidity can vanish when it is needed. Dependence on a few data and cloud vendors adds concentration risk. Supervisors such as IOSCO stress governance, testing, monitoring and human oversight, with controls such as kill switches.

Key formulas to remember

Implementation shortfall: Implementation shortfall = (Execution price − Decision price) × Shares for a buy order; For a sell order reverse the sign. Includes impact, spread and delay costs. Opportunity cost of unfilled shares is extra.
Quoted bid-ask spread: Spread = Ask − Bid; Half-spread = Spread ÷ 2; A market maker earns about the half-spread on each side if trades are uninformed.
Mid-price: Mid = (Bid + Ask) ÷ 2; Used as the benchmark to measure effective spread.
Effective spread: Effective spread = 2 × |Trade price − Mid|; Measures actual cost paid, which can differ from the quoted spread.
Overfitting check: Out-of-sample performance << in-sample performance signals overfitting; A rule of thumb for interpretation, not a statistical test.

How to solve AI Use Cases in Trading and Market Making questions

Use this sequence for any scenario question on AI in trading and market making.

1Identify the activity: price prediction, execution, market making or liquidity provision.
2Name the objective the AI serves: lower cost, better signal, tighter spread or inventory control.
3State the benefit in normal markets, such as lower impact, faster reaction or tighter quotes.
4Identify the risk the scenario hints at: overfitting, herding, regime change, model opacity, data or vendor concentration.
5Check market conditions. If stress, ask whether liquidity may withdraw or correlated algorithms may amplify moves.
6Pick the control: independent validation, out-of-sample testing, monitoring, kill switches, human oversight, limits.
7If numbers are given, compute the cost or spread first, then interpret it.
8Eliminate options that overstate AI as risk-free or as always improving liquidity.

Quickest way: Use case, benefit, risk, control

When to use it: For conceptual MCQs where you have about a minute per question.

Label the use case in the stem.
Recall its one main benefit.
Look for the matching risk keyword in the options.
Choose the option that pairs the risk with a governance control, not a claim of elimination.
For numbers, apply half-spread or implementation shortfall directly.

Common mistakes in AI Use Cases in Trading and Market Making

Assuming AI always improves market liquidity.
Tighter spreads in calm markets look like proof of better liquidity.
Fix: Remember liquidity can be fragile. Automated market makers may widen quotes or withdraw in stress.
Confusing inventory risk with adverse selection.
Both cause market maker losses.
Fix: Inventory risk is price moves on positions held. Adverse selection is trading against better informed counterparties.
Treating good back-test results as proof of a good model.
In-sample fit looks convincing.
Fix: Look for out-of-sample and live testing. A large gap points to overfitting.
Mixing up quoted and effective spread.
Both are called spread.
Fix: Quoted is Ask − Bid. Effective is twice the distance of the trade price from the mid.
Ignoring herding when many firms use similar AI.
Students judge each model alone.
Fix: Think at system level: similar models and data can create correlated trades and amplify shocks.
Choosing 'remove human oversight' as a control.
Automation seems the point of AI.
Fix: Supervisory guidance favours governance, testing, monitoring and human accountability.

Worked examples

Example 1

A fund decides to buy 50,000 shares when the price is $40.00. An AI execution algorithm buys all shares at an average price of $40.12. What is the implementation shortfall in dollars, ignoring fees and unfilled shares?

Show the solution

Order is a buy, so shortfall = (Execution price − Decision price) × Shares.
Price difference = 40.12 − 40.00 = $0.12.
Multiply by shares: 0.12 × 50,000 = $6,000.

Answer: $6,000 (about 30 bps of the $2,000,000 decision value).

Example 2

A market maker's AI quotes bid $99.90 and ask $100.10. A customer buys at $100.10. A trade then occurs at $99.90 from a seller. Which is correct for the effective spread on the first trade, and what does it show?

Show the solution

Mid = (99.90 + 100.10) ÷ 2 = $100.00.
First trade: |100.10 − 100.00| = $0.10.
Effective spread = 2 × 0.10 = $0.20.
Quoted spread = 100.10 − 99.90 = $0.20, so the trade paid the full quoted spread.
Interpretation: the market maker earned the half-spread of $0.10 per share if the trade was uninformed.

Answer: Effective spread is $0.20, equal to the quoted spread; the market maker earns $0.10 per share if it is not adversely selected.

Exam tips

Expect scenario MCQs where you must pair a use case with its main risk. Learn the pairs.
Watch for stress scenarios: the correct option usually mentions liquidity withdrawal or amplification.
Be ready for IOSCO-style governance answers: testing, monitoring, oversight and accountability.
For numerical items, check the sign for buys versus sells and whether the question asks for quoted or effective spread.

Practice questions from Advances in Artificial Intelligence: Implications for Capital Markets Activities