A hybrid approach for portfolio selection with higher-order moments: Empirical evidence from Shanghai Stock Exchange
作者：
Highlights：
• A portfolio selection problem with higher-order moments is considered.
• Machine learning algorithms are applied for data analysis and prediction in the stock market.
• Genetic algorithm is used to solve the multi-objective optimization problem.
• The out-of-sample performance of our model is significantly better than those of traditional ones.
• Robustness is checked, compared with another two existing methods.
摘要
•A portfolio selection problem with higher-order moments is considered.•Machine learning algorithms are applied for data analysis and prediction in the stock market.•Genetic algorithm is used to solve the multi-objective optimization problem.•The out-of-sample performance of our model is significantly better than those of traditional ones.•Robustness is checked, compared with another two existing methods.
论文关键词：Portfolio optimization,Higher-order moments,Genetic algorithm,Machine learning algorithm
论文评审过程：Received 27 August 2019, Revised 21 November 2019, Accepted 27 November 2019, Available online 2 December 2019, Version of Record 11 December 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.113104
