Multiple financial analyst opinions aggregation based on uncertainty-aware quality evaluation

Abstract

Financial analysts’ opinions are pivotal in investment decision-making, as they provide valuable expert knowledge. Aggregating these opinions offers a promising way to unlock their collective wisdom. However, existing opinion aggregation methods are hindered by their inability to effectively assess differences in opinion quality, resulting in suboptimal outcomes. This Study introduces a novel model called SmartMOA, which addresses this limitation by automatically evaluating the quality of each opinion and integrating this evaluation into the aggregation process. Our model begins with a novel Bayesian neural network that leverages the implicit knowledge embedded in the interactions between analysts and stock characteristics. This methodology produces an assessment of individual opinions that accounts for uncertainties. We then formulate a bi-objective combinatorial optimization problem to determine optimal weights for combining multiple analysts’ opinions, simultaneously minimizing the error and uncertainty of the aggregated outcome. Therefore, SmartMOA systematically highlights high-quality opinions during the aggregation process. Using a real dataset spanning eight years, we present comprehensive empirical evidence that demonstrates the superior performance of SmartMOA in heterogeneous analyst opinion aggregation.