FRM Part II · FRM Exam Part II · Advances in Artificial Intelligence: Implications for Capital Markets Activities
A bank uses a reinforcement learning agent to execute large equity orders, minimizing implementation shortfall. During back-testing, it performs well, but in live trading during a volatility spike it behaves erratically. Which explanation best fits this outcome?
The most plausible explanation is distribution shift: the agent learned from historical market regimes, so in an unfamiliar volatility spike its learned policy generalizes poorly, producing erratic behavior despite strong back-test results.
- AThe agent was trained on historical regimes and faces distribution shift in unseen conditionsCorrect
- BReinforcement learning cannot be used for execution by definition
- CImplementation shortfall is unaffected by market volatility
- DThe agent had too little leverage to adapt
Explanation
Learning-based agents optimize on training data and may generalize poorly when live conditions differ (distribution shift or regime change). Good back-test results therefore do not guarantee stress performance. Volatility clearly affects implementation shortfall, and leverage is not the issue.
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