Machine learning Analysts’ Sentiment Industry Factor
Log in to collectAcademic paper
Artificially Intelligent Analyst Sentiment and Aggregate Market Behavior
Vidhi Chhaochharia; Alok Kumar; Ville Rantala; Alan Zhang
- University of Miami
- ?University of Miami - Department of Finance
- ?University of Miami - Miami Herbert Business School
- Florida International University
- ?Florida International University (FIU)
Strategy in a nutshell
The strategy leverages analyst earnings forecasts and historical firm-level data to predict forecast errors using neural networks (NN). The dataset combines I/B/E/S quarterly forecasts, CRSP stock data, and Compustat firm attributes. A two-layer feed-forward NN with 15 hidden neurons is trained for each analyst to predict firm-level forecast errors based on their lagged errors. Predictions are aggregated to firm, industry, and market levels, and compared against linear regression benchmarks.
Using these predictions, the study decomposes market-level forecast errors into a predictable
Economic rationale
The strategy works because it captures systematic patterns and biases in analysts’ forecasting behavior that traditional linear models cannot. By training neural networks at the individual analyst level, the model learns both linear and non-linear relationships in past forecast errors, including analyst-specific