Author
Abstract
There are a lot of fuzzy uncertainties in the real financial market, and fuzzy numbers can well depict this phenomenon. On the one hand, compared with fuzzy numbers (only with membership function), intuitionistic fuzzy numbers (with both membership and non-membership functions) can describe different types of uncertainties more accurately by providing a more comprehensive information description. On the other hand, investors are not entirely rational, and their attitudes are crucial to investment decisions. Based on these facts, we define a new coherent intuitionistic fuzzy number with two parameters, which can describe markets’ fuzzy uncertainties and investors’ attitudes at the same time. Then, we derive specific expressions of its possibilistic mean, possibilistic variance, and possibilistic covariance through rigorous mathematical proofs. In addition, too concentrated allocation of investment weights is not conducive to risk diversification, while entropy can improve portfolio diversity because it does not depend on the normal distribution assumption of security returns. Considering this advantage of entropy, we construct a new mean–variance-entropy portfolio model which incorporates two-parameter coherent intuitionistic fuzzy numbers. Further, we employ the ideal point method to solve this model. Finally, we empirically demonstrate the feasibility and effectiveness of our new model by randomly selecting 20 stocks from the Chinese stock market. The results show that different values of the two parameters can describe the ambiguity of the market and the different attitudes of investors: investors with an optimistic-optimistic attitude tend to achieve higher returns and greater diversification, while optimistic-neutral investors tend to undertake lower risks.
Suggested Citation
Xue Deng & Fengting Geng, 2025.
"A Novel Mean–Variance-Entropy Portfolio with Two-Parameter Coherent Triangular Intuitionistic Fuzzy Number,"
Computational Economics, Springer;Society for Computational Economics, vol. 66(4), pages 3081-3130, October.
Handle:
RePEc:kap:compec:v:66:y:2025:i:4:d:10.1007_s10614-024-10773-4
DOI: 10.1007/s10614-024-10773-4
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:66:y:2025:i:4:d:10.1007_s10614-024-10773-4. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.