A stock selection algorithm hybridizing grey wolf optimizer and support vector regression
作者：
Highlights：
• A new stock selection algorithm is proposed.
• Grey wolf optimizer is innovatively hybridized with support vector regression.
• The performance of hybridizing different meta-heuristics is compared.
• The new algorithm can achieve excess returns in American and Chinese markets.
• The effectiveness and superiority of the new algorithm are empirically examined.
摘要
•A new stock selection algorithm is proposed.•Grey wolf optimizer is innovatively hybridized with support vector regression.•The performance of hybridizing different meta-heuristics is compared.•The new algorithm can achieve excess returns in American and Chinese markets.•The effectiveness and superiority of the new algorithm are empirically examined.
论文关键词：Quantitative investment,Portfolio optimization,Stock selection,Grey wolf optimizer,Support vector regression
论文评审过程：Received 28 September 2020, Revised 22 March 2021, Accepted 17 April 2021, Available online 23 April 2021, Version of Record 6 May 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115078
