A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing

作者：

Highlights：

• A dynamic asset allocation system is developed to improve smart beta investing.

• Hidden Markov models is used to detect market cycles and optimise smart beta portfolios.

• Several types of portfolios are proposed and evaluated on single and multi-regime systems.

• An embedded feature selection algorithm is integrated to improve regime identification.

• Performance of the proposed models is evaluated on MSCI factor indices.

摘要

•A dynamic asset allocation system is developed to improve smart beta investing.•Hidden Markov models is used to detect market cycles and optimise smart beta portfolios.•Several types of portfolios are proposed and evaluated on single and multi-regime systems.•An embedded feature selection algorithm is integrated to improve regime identification.•Performance of the proposed models is evaluated on MSCI factor indices.

论文关键词：Hidden Markov Model,Dynamic asset allocation,Portfolio optimization,Feature selection,Smart beta

论文评审过程：Received 5 November 2019, Revised 4 May 2020, Accepted 2 July 2020, Available online 15 July 2020, Version of Record 11 August 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113720