One of the main challenges facing researchers and industry professionals for decades is the successful prediction of asset returns. This paper enriches this endeavor by applying topological metrics of correlation networks to the challenge of financial forecasting. These network-based metrics are retrieved with the help of graph theory and quantify the interconnectedness of financial assets. In this paper, we show that this network-based information statistically significantly predicts future asset returns. Because industry professionals are more interested in the economic value-added of competing forecasting approaches, we also devote our attention to an economic analysis. Considering economic performance metrics, network-based predictors generate a clear value-added, which also applies to the multi-asset allocation case.

中文翻译：

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使用基于网络的指标预测资产回报：统计和经济分析

几十年来，研究人员和行业专业人士面临的主要挑战之一是成功预测资产回报。本文通过将相关网络的拓扑度量应用于金融预测的挑战，丰富了这一努力。在图论的帮助下检索这些基于网络的指标，并量化金融资产的相互关联性。在本文中，我们表明这种基于网络的信息在统计上显着地预测了未来的资产回报。由于行业专业人士对竞争性预测方法的经济附加值更感兴趣，因此我们也将注意力集中在经济分析上。考虑到经济绩效指标，基于网络的预测器会产生明确的增值，这也适用于多资产配置情况。