Comparing Analyst Consensus Data for Forecast Accuracy and Stock Selection

Summary

This analysis compares analyst forecast data from two Chinese financial data providers, examining report coverage, forecast accuracy, and the usefulness of forecast-related factors in stock selection. One provider is described as covering more stocks, while the other has improved its accuracy in recent years, particularly for larger companies. The comparison emphasizes that extreme forecast errors can materially affect results: without filtering, one source is more accurate, while cleaned data from the broader source can perform better on accuracy measures.

For factor applications, the report says broad fundamental factors have similar information coefficients across the sources, while one source performs better in full-market return prediction and multifactor portfolios. Surprise-based strategies show mixed results: one source has stronger reported significance in factor tests, while the other does better over some medium-term holding periods. These findings are specific to the study’s data and methods; changing coverage, outliers, and provider revisions limit generalization. The report flags market, liquidity, and model risks.

Key ideas

• The study compares two providers’ analyst forecast coverage, accuracy, and stock-selection usefulness.
• One provider covers more stocks, while the other has recently improved forecast accuracy, especially for larger companies.
• Removing extreme forecasts can change which provider appears more accurate.
• The sources produce different results for broad fundamental factors and forecast-surprise strategies.
• Provider coverage and data revisions can affect results, so findings may not generalize to other periods or methods.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.