Problem
Scenario
You are asked to build a model that predicts stock prices from historical market data. The goal is to produce forecasts that could support downstream investment research, while avoiding common mistakes like leakage and unrealistic backtests.
Question
How would you approach building a predictive model for stock prices?
Representative Dataset
size·8 years of daily data for 520 equities, about 1.05M ticker-day rowstarget·Next-day log return, plus optional next-day closefeatures·OHLCV, lagged returns, rolling stats, momentum, sector and macro contextmissing_data·Sparse market gaps and release-driven macro missingness
Practicing as: Data Scientist interview at FactsetHi, I'll play your Factset interviewer for the Data Scientist role. Candidates describe these interviews as mostly positive and moderately difficult, so expect me to be friendly and conversational. Take your time with the question above and answer like we're in the room.
You are practicing as a guest. Sign up free to get your answer graded with AI feedback. Your draft stays right here.
