Cross-Validation Quant Interview Guide
Cross-validation quant interview guide for folds, validation estimates, leakage, time-series caveats, examples, and model-selection mistakes.
Candidates validating models under limited data and noisy signals.
Cross-validation reuses data carefully
Cross-validation trains and evaluates across multiple splits to estimate model performance under limited data. The goal is a more stable validation view than a single split can provide.
Folds must respect the problem
Random folds can be reasonable for independent observations, but time-ordered financial data often needs chronological or blocked validation. The split design must avoid leakage.
Concrete example
If yesterday and today are highly related, randomly placing one in train and one in validation can inflate performance. A chronological split may better reflect live use.
Use it for model selection
Cross-validation can help choose features, penalties, or hyperparameters. Keep a final holdout when possible, because repeated tuning on validation folds can still overfit.
Common mistakes
Candidates often say use k-fold cross-validation without checking time order, leakage, or repeated model selection. Validation design is part of the answer.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.