R-Squared Quant Interview Guide
R-squared quant interview guide for variance explained, model fit, prediction limits, adjusted interpretation, examples, and mistakes.
Candidates interpreting regression fit and model usefulness.
R-squared measures in-sample fit
R-squared describes the share of variation in the outcome explained by the fitted regression in the sample. It is a fit statistic, not a direct proof of prediction quality or economic value.
High fit can still disappoint
A model can have high R-squared because it uses many features, fits stale structure, or explains variation that is irrelevant to the decision. Out-of-sample testing matters more than fit alone.
Concrete example
A model explaining most historical price variation may still be poor for trading if it does not forecast future changes after costs. The target and decision horizon define usefulness.
Use adjusted language
If features are added, adjusted R-squared or validation metrics can give a more cautious view. The interview answer should explain why adding variables can mechanically improve in-sample fit.
Common mistakes
Candidates often say a high R-squared means the model is good. A stronger answer asks good for what, under what sample, and whether the result survives validation.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.