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This is a medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at Point72.

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This question is commonly asked for Machine Learning Engineer candidates at Point72 during technical interviews.

Design Features for Residual Volatility

Last updated: Apr 22, 2026

Quick Overview

This question evaluates competency in feature engineering for financial time-series, volatility forecasting after removing systematic market effects, cross-sectional and sector-based information use, time-series model selection and validation, and representation learning decisions such as autoencoder latent bottleneck sizing.

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Design Features for Residual Volatility

Point72

Apr 14, 2026, 12:00 AM

mediumMachine Learning EngineerTechnical ScreenMachine Learning

You have historical intraday data for a universe of equities. Design features and a modeling approach to predict a target stock's volatility over the next 30 minutes after removing systematic market effects. Explain how you would:

define the prediction target,
engineer features from the stock itself,
use information from related stocks or sector peers,
choose an appropriate model and validation strategy for time-series data, and
if you use an autoencoder, decide the size of the latent bottleneck.

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