Manifold feature index: A novel index based on high-dimensional data simplification

作者：

Highlights：

• Manifold feature (MF) index is developed to reflect the activity of the stock market.

• Features of the eigenvectors of Laplace–Beltrami operator are taken as constituents.

• MF indexes are closer to the stock market than the compared index series.

摘要

•Manifold feature (MF) index is developed to reflect the activity of the stock market.•Features of the eigenvectors of Laplace–Beltrami operator are taken as constituents.•MF indexes are closer to the stock market than the compared index series.

论文关键词：Stock index,Constituent selection,Manifold learning,High-dimensional data simplification

论文评审过程：Received 2 May 2020, Revised 2 June 2021, Accepted 19 March 2022, Available online 29 March 2022, Version of Record 5 April 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116957