Abstract
AbstractOne 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
