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.

中文翻译：

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基于不确定性感知质量评价的多个财经分析师意见聚合


金融分析师的意见在投资决策中至关重要，因为它们提供了宝贵的专业知识。将这些观点汇总起来提供了一种很有前途的方式来释放他们的集体智慧。然而，现有的意见汇总方法由于无法有效评估意见质量的差异而受到阻碍，导致结果欠佳。本研究引入了一种称为 SmartMOA 的新模型，它通过自动评估每个意见的质量并将此评估集成到聚合过程中来解决这一限制。我们的模型从一个新的贝叶斯神经网络开始，该网络利用了嵌入在分析师和股票特征之间交互中的隐含知识。这种方法对个人意见进行了评估，从而考虑了不确定性。然后，我们制定了一个双目标组合优化问题，以确定结合多个分析师意见的最佳权重，同时最大限度地减少聚合结果的误差和不确定性。因此，SmartMOA 在聚合过程中系统地突出了高质量的意见。使用跨越八年的真实数据集，我们提供了全面的实证证据，证明了 SmartMOA 在异构分析师意见聚合中的卓越性能。