Artificially Intelligent Analyst Sentiment and Aggregate Market Behavior
70 Pages Posted: 6 Dec 2022 Last revised: 15 Mar 2023
Date Written: March 14, 2023
This study develops a new machine learning-based measure of aggregate analyst sentiment. We first train analyst-specific neural network models that capture each analyst's predictable forecast bias across firms and then aggregate the information at the industry and market levels. We decompose the aggregated forecast errors of analysts into predictable and non-predictable components, and interpret the non-predictable component as a measure of analyst sentiment. Variation in analyst sentiment along the business cycle suggests that they systematically underreact to macroeconomic information. A Long-Short trading strategy based on industry-level analyst sentiment earns an annualized alpha of over 7%.
Keywords: analyst biases, earnings forecasts, aggregate analyst sentiment, return predictability, machine learning
JEL Classification: C45, G14, G24
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