Identifying predictors of analyst rating quality: An ensemble feature selection approach

Abstract

Forecasting the analyst rating quality (ARQ), defined as whether a stock rating provided by an analyst can correctly foretell the stock movement, is crucial to fully leveraging the value of this information resource. This study develops a two-phase method to identify key predictors for ARQ forecasting. In the first stage, we conduct a thorough literature review to obtain a comprehensive list of candidate features, and organise them under three categories: analyst-related, rating-related, and stock-related. In the second stage, we propose a heterogeneous community-based ensemble feature selection method (ComEFS), with the goal of identifying a subset of relevant predictors to be jointly used for ARQ forecasting. Thorough experiments are conducted on a real dataset to verify the effectiveness of our proposed method. The empirical results show that key predictors identified by ComEFS exhibit stronger predictive power compared to those identified by benchmark methods. This study provides insights about ARQ forecasting by selecting the right input. Selectively utilizing these predictive features can help improve the performance of downstream machine learning models and ultimately help investors avoid unreliable analyst ratings and financial loss.