Fuzzy Portfolio Selection Using Stochastic Correlation

Gumsong Jo (), Hyokil Kim, Hoyong Kim and Gyongho Ri
Additional contact information
Gumsong Jo: Kim Il Sung University
Hyokil Kim: Kim Il Sung University
Hoyong Kim: Kim Il Sung University
Gyongho Ri: Kim Il Sung University

Computational Economics, 2024, vol. 63, issue 4, No 8, 1493-1509

Abstract: Abstract Here we have proposed fuzzy portfolio selection model using stochastic correlation (FPSMSC) to overcome limitations both in fuzzy and stochastic world. The newly proposed model not only gets harmonious efficient frontier, but also considers the future movement of stock prices based on fuzzy expertise knowledge. The investment weights of the model have been optimized based on the monthly return data of 18 stocks listed in S&P500 from October 2011 to September 2015. The proposed model has provided higher returns in the whole regime of risk for the period from October 2014 to September 2015, whose monthly return data are used as training data than other available portfolio selection models, i.e., fuzzy portfolio selection models with credibility and possibility and statistic model. Also, the present model has shown the better smoothness of the variations of returns with respect to risk aversion parameter, λ, from the monthly data from October 2015 to September 2016, which is not included to training database. Especially, our model is superior to other models in the regime of 0–0.3 for the risk aversion level. It is demonstrating that the FPSMSC is efficient for the investors who tend to seek the high return in portfolio management.

Keywords: Fuzzy portfolio selection; Stochastic correlation; Credibility measure; Possibility measure; Efficient frontier (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-023-10371-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:kap:compec:v:63:y:2024:i:4:d:10.1007_s10614-023-10371-w

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-023-10371-w

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().