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

A trading firm wants to group its clients into segments by trading behavior (frequency, instrument mix, holding period) without any predefined labels. Which machine learning approach is most appropriate?

Unsupervised learning, such as clustering, is most appropriate. The firm has no predefined labels and wants to discover natural groupings from behavioral features. Supervised methods need labeled targets, and reinforcement learning requires an agent learning from rewards through interaction, which does not fit this segmentation task.

  1. ASupervised classification using labeled segment names as targets
  2. BUnsupervised learning such as clustering to discover natural groupings in the dataCorrect
  3. CReinforcement learning with a reward for each trade executed
  4. DSupervised regression on future trading volume

Explanation

With no predefined labels, the task is to discover structure in the data, which is unsupervised learning; clustering is the standard technique. Supervised methods need labeled targets, and reinforcement learning requires an agent acting in an environment with rewards.

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