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Predict Stock Returns from Sentiment and Market Data

GsaData Scientist · Technical Screen
HardData ScientistTechnical ScreenMachine Learning

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This problem tests the ability to design an end-to-end predictive pipeline for stock returns, focusing on time-series integrity, feature engineering, and robust evaluation metrics.

Scenario

You are tasked with building a predictive model for daily stock returns (t to t+1) for a universe of large-cap stocks. Your input data includes daily sentiment scores (from news headlines), relevance scores, price data (OHLC), and index membership. The goal is to output a signal that translates to a market-neutral portfolio strategy.

Task

Design a machine learning system to predict the target variable: y = (Price(t+1) - Price(t)) / Price(t). Provide a structured response covering the following:

  1. Data and Features: How do you structure the training data to ensure no future information is leaked? List at least three specific features you would derive from the raw sentiment and price data.
  2. Model Architecture: Given the high noise-to-signal ratio in financial data, what type of baseline model would you start with, and what advanced model would you transition to? Justify your choice.
  3. Evaluation Strategy: Explain how you would perform offline backtesting. Why is a standard K-fold cross-validation insufficient for this task?
  4. Deployment and Monitoring: How do you detect concept drift in real-time, and what is your strategy for model retraining or fallback mechanisms when the sentiment signal fails?

Constraints

  • Assume 5,000+ liquid stocks.
  • Sentiment scores are provided at irregular intervals during the day.
  • The objective is to minimize Mean Squared Error (MSE) while maintaining a positive Information Coefficient (IC).

Hints

Hint 1

Consider using walk-forward validation (sliding windows) instead of random shuffling to preserve the temporal order of financial data.

Hint 2

Think about how to normalize sentiment scores using the relevance score and how to account for the impact of market-wide volatility on individual stock returns.

Hint 3

For deployment, consider the lag between news publication and market impact, and how to monitor the distribution of your model's output residuals versus historical variance.

Follow-up questions

  • How would you handle missing sentiment data for a stock on a specific day without introducing look-ahead bias?
  • If your model starts showing strong performance decay, how do you distinguish between 'regime change' and 'model degradation'?
  • How would you incorporate transaction costs into your offline evaluation to ensure your predicted returns are actually actionable?