Iterative Filtering Algorithms for Computing Consensus Analyst Estimates

Kheng Kua, A. Ignjatović
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引用次数: 2

Abstract

In equity investment management, sell side analysts serve an important role in forecasting metrics of companies’ financial performance. These estimates are often produced in an opaque manner, namely, the process upon which the estimate is initiated or revised is not directly observable. With multiple analysts covering the same company, and an analyst covering multiple companies, we have an n-m relationship. The systematic capture of analyst estimates provide a systematic and quantitative proxy for market sentiment. Thus far the academic literature analysing this dataset has resolved to use relatively simple methods for aggregating the individual estimates to arrive at a consensus estimate.In this paper we propose a novel method for aggregating analyst estimates utilising iterative filtering algorithms. This work is inspired by applications of such classes of algorithms to the robust aggregation of sensor network data and online reviews. We conduct experiments using real-world datasets to demonstrate the efficacy of this approach. The results suggest iterative filtering methods improve upon the forecast accuracy of the consensus forecast compared to the simple mean consensus.
计算共识分析师估计的迭代滤波算法
在股权投资管理中,卖方分析师在预测公司财务业绩指标方面发挥着重要作用。这些评估通常是以不透明的方式产生的,也就是说,启动或修改评估的过程不是直接可观察到的。如果多个分析师研究同一家公司,而一个分析师研究多家公司,我们就有了n-m关系。系统地捕捉分析师的估计为市场情绪提供了系统和定量的代理。到目前为止,分析该数据集的学术文献已经决定使用相对简单的方法来汇总个人估计,以达成共识估计。在本文中,我们提出了一种利用迭代滤波算法聚合分析师估计的新方法。这项工作的灵感来自于这类算法在传感器网络数据和在线评论的鲁棒聚合中的应用。我们使用真实世界的数据集进行实验来证明这种方法的有效性。结果表明,与简单平均共识相比,迭代滤波方法提高了共识预测的预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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