Articles


Document Type
Journal article (JA)
Title
A hybrid approach for portfolio selection with higher-order moments: Empirical evidence from Shanghai Stock Exchange
Author
Chen, Bilian (1, 2); Zhong, Jingdong (3); Chen, Yuanyuan (4)
作者单位
(1) Department of Automation, Xiamen University, Xiamen; 361005, China (2) Xiamen Key Lab. of Big Data Intelligent Analysis and Decision-making, Xiamen; 361005, China (3) School of Economics, Peking University, Beijing; 100871, China (4) Department of Finance and Insurance, Nanjing University, Jiangsu Province; 210093, China
RPAddress
Nanjing Univ, Dept Finance & Insurance, Nanjing 210093, Jiangsu, Peoples R China.
Email
ResearchID
ORCID
Journal
Expert Systems with Applications
Publisher
Elsevier Ltd
ISSN
0957-4174
Published
2020-05-01, 145 ():-.
JCR
ImpactFactor
ISBN
Fund_Code
National Natural Science Foundation of ChinaNational Natural Science Foundation of China [61772442, 11671335, 61836005]
会议名称
会议地点
会议开始日期
会议结束日期
HYLWLB
HYJB
Keywords
Distribution functions; Electronic trading; Financial markets; Genetic algorithms; Higher order statistics; Investments; Learning algorithms; Learning systems; Machine learning; Multiobjective optimization; Radial basis function networks
Abstract
Skewness and kurtosis, the third and fourth order moments, are statistics to summarize the shape of a distribution function. Recent studies show that investors would take these higher-order moments into consideration to make a profitable investment decision. Unfortunately, due to the difficulties in solving the multi-objective problem with higher-order moments, the literature on portfolio selection problem with higher-order moments is few. This paper proposes a new hybrid approach to solve the portfolio selection problem with skewness and kurtosis, which includes not only the multi-objective optimization but also the data-driven asset selection and return prediction, where the techniques of two-stage clustering, radial basis function neural network and genetic algorithm are employed. With the historical data from Shanghai stock exchange, we find that the out-of-sample performance of our model with higher-order moments is significantly better than that of traditional mean-variance model and verify the robustness of our hybrid algorithm. ? 2019
WOS Categories
Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science
Accession Number
WOS:000518692500007
EI收录号
20195007834599
DOI
10.1016/j.eswa.2019.113104
ESI_Type
ENGINEERING
Collection
SCIE, EI, SSCI, CPCI-S, CPCI-SSH

Back to List