Asset selection via correlation blockmodel clustering

作者：

Highlights：

• Cluster financial assets to identify a small set of stocks of large diversification.

• Develop a data-driven approach to clustering based on a correlation blockmodel.

• Devise algorithms to detect clusters with theory and practical guidance.

• Conduct empirical analysis to verify the performance of the algorithms.

摘要

•Cluster financial assets to identify a small set of stocks of large diversification.•Develop a data-driven approach to clustering based on a correlation blockmodel.•Devise algorithms to detect clusters with theory and practical guidance.•Conduct empirical analysis to verify the performance of the algorithms.

论文关键词：Asset selection,Cluster analysis

论文评审过程：Received 28 August 2021, Revised 29 December 2021, Accepted 16 January 2022, Available online 5 February 2022, Version of Record 10 February 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116558