An Accelerated Optimization Approach for Finding Diversified Industrial Group Stock Portfolios with Natural Group Detection

Chun-Hao Chen (), Jonathan Coupe and Tzung-Pei Hong ()
Additional contact information
Chun-Hao Chen: Department of Computer Science and Information Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Jonathan Coupe: Department of Computer Science and Information Engineering, Tamkang University, Taipei 25137, Taiwan
Tzung-Pei Hong: Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 81148, Taiwan

Mathematics, 2023, vol. 11, issue 14, 1-25

Abstract: Stock portfolio optimization is always an interesting and attractive research problem due to the variety of stock markets. To find a useful stock portfolio, metaheuristic-based approaches have been presented to obtain diverse group stock portfolios (DGSPs) by considering the diversity of stock portfolios in the past. However, in the existing DGSP algorithms, two problems remain to be solved. The first is how to set a suitable group size, and the second is that the evolution process is time-consuming. To solve these problems, in this paper, an approach using grouping genetic algorithms (GGAs) was proposed for optimizing a DGSP. For setting a suitable group size, the proposed approach utilized two attributes of group stocks, including the return on equity and the price/earnings ratio. Then, to derive better stock groups, a cluster validation factor was designed, which was used as part of a fitness function. To solve the time-consumption problem, using the designed temporary chromosome, the number of stock portfolios that need to be evaluated could be reduced in the proposed approach to speed up the evolution process. Finally, experiments on two real stock datasets containing 31 and 50 stocks were conducted to show that the proposed approach was effective and efficient. The results indicated that the proposed approach could not only achieve similar returns but also accelerate the evolution process when compared with the existing algorithms.

Keywords: clustering; cluster validation indices; portfolio optimization; grouping genetic algorithm; genetic algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/14/3144/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/14/3144/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:14:p:3144-:d:1195586

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().