News Sentiment Quant Interview Guide
News sentiment quant interview guide covering sentiment extraction, timestamping, events, leakage, evaluation, examples, and caveats.
Candidates discussing text signals, event timing, and market reaction.
News sentiment converts text into features
A sentiment workflow maps articles or headlines to entities, timestamps, event types, and numeric or categorical features.
Timestamping controls leakage
The model should use when the news became available to the strategy, not only the event date or article date shown later.
Concrete example
A negative headline after market close may affect next-day trading, while the same headline during the session has different execution timing.
Evaluate decay and surprise
News signals can decay quickly, depend on whether information was expected, and interact with liquidity and event risk after release.
Common mistakes
Candidates often treat sentiment scores as truth. Scores are noisy features that require entity matching and out-of-sample validation.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.