Explain factor leakage checks and IC/ICIR filtering

Q: Explain factor leakage checks and IC/ICIR filtering

This is a Machine Learning interview question from Citadel for Data Scientist roles. View the full question and solution on PracHub.

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Question

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You’re interviewing for a quantitative/alpha role and have built predictive factors (features) for returns.

Answer the following (conceptual) questions clearly:

How do you determine whether a factor leaks future information (uses “future data”) ?
- Give concrete examples of common leakage sources in financial datasets.
- Describe how you would test for leakage.
How would you use IC and ICIR to select factors ?
- Define IC and ICIR (state any assumptions, e.g., cross-sectional vs time-series).
- Describe a practical factor screening and monitoring workflow.
If you train a model using factors, how do you train it and what is the objective ?
- Describe the training setup (labels, horizon, universe, sampling).
- Provide typical objectives used in alpha modeling.
How do you prevent overfitting in factor/model research ?
- Cover data-splitting methodology specific to time series and finance.
- Mention regularization / model complexity controls and research-process controls.

Explain factor leakage checks and IC/ICIR filtering

Solution

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