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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, minimising implementation shortfall. During back-testing the agent performs very well, but in live trading it underperforms during a sudden volatility spike. Which explanation best fits the risk management concern?

The best explanation is distribution shift. The agent learned from historical market conditions, so in a new regime such as a sudden volatility spike its learned behaviour may not generalise, causing live performance to fall short of back-tested results.

  1. AThe agent was trained on historical regimes and may not generalise to unseen market conditions (distribution shift)Correct
  2. BReinforcement learning cannot be used with historical data
  3. CThe agent had too few parameters to capture any pattern
  4. DImplementation shortfall is not measurable for equity orders

Explanation

Learning-based execution agents optimise on past market dynamics, and performance can degrade when conditions differ, such as a volatility spike. This is distribution shift or regime change risk. The other options are factually wrong: simulation on historical data is standard and shortfall is measurable.

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