Keywords

analyst forecasts, financial signals, forecast diversity, consensus accuracy, machine learning

Abstract

We employ a novel machine learning technique to classify analysts’ forecast revisions into five types based on how the revision weighs publicly available signals. We label these forecast types as quant, sundry, contrarian, herder, and independent forecasts. Our tests reveal that a greater diversity of forecast types within the consensus is associated with increased consensus dispersion and improved consensus accuracy. Additionally, consensus diversity is associated with an improved information environment for firms, as reflected in reduced earnings announcement information asymmetry and volatility, higher earnings response coefficients, and faster price formation. Our study sheds light on how analysts revise their forecasts and documents capital market benefits associated with different analyst forecasting approaches.

Original Publication Citation

“Classifying Forecasts,” with Robbie Moon and James Warren. The Accounting Review, 99 (6), 2024.

Document Type

Peer-Reviewed Article

Publication Date

2024-5

Publisher

The Accounting Review

Language

English

College

Marriott School of Business

Department

Accountancy

University Standing at Time of Publication

Full Professor

Download

Included in

Accounting Commons

Share

COinS