Transparent prediction of financial analyst recommendation quality using generalized additive model

Abstract

Financial analysts play a key role in financial decision-making, but the reliability of their recommendations can fluctuate dramatically depending on changes in analyst competence and contextual dynamics, posing a significant challenge to investors seeking guidance. This study unveils a novel explainable deep learning architecture, termed Quality Attribution Network (QuANet), which innovates by integrating a Generalized Additive Model framework, amplifying prediction accuracy and facilitating an in-depth understanding of how distinct variables contribute to the quality of analyst recommendations. Further, QuANet incorporates an attention mechanism to discern salient features, thereby ensuring that critical analyst, rating, and stock information receives appropriate weight. Empirical validation on extensive datasets corroborates QuANet’s superiority over existing benchmarks across diverse quality prediction metrics. Enhancing predictive capability translates into tangible gains for investment strategies, underscoring the model’s practical applicability. Additionally, QuANet’s attribution capabilities enable nuanced differentiation between analysts, pinpointing those endowed with genuine expertise within the financial advisory landscape. In sum, this research advances the analytical toolkit for assessing analyst recommendations by introducing a model that harmonizes predictive prowess with interpretative clarity. Investors stand to benefit from the transparent insights generated, facilitating the extraction of valuable knowledge from analyst recommendations to inform judicious investment decisions.