Behavioural perspectives in forex portfolio value analysis
This paper combines Cumulative Prospect Theory (CPT) and the Grey Clustering Algorithm (GCA) to guide the optimization of forex portfolio selection. The United States Dollar (USD) against a universe of 84 other currencies was used for portfolio value analysis using the Differential Evolution Algorit...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | Cogent Economics & Finance |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23322039.2025.2494135 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849709966099742720 |
|---|---|
| author | Kofi Agyarko Ababio Necati Alp Erilli Eric Nkansah Jules Clement Mba |
| author_facet | Kofi Agyarko Ababio Necati Alp Erilli Eric Nkansah Jules Clement Mba |
| author_sort | Kofi Agyarko Ababio |
| collection | DOAJ |
| description | This paper combines Cumulative Prospect Theory (CPT) and the Grey Clustering Algorithm (GCA) to guide the optimization of forex portfolio selection. The United States Dollar (USD) against a universe of 84 other currencies was used for portfolio value analysis using the Differential Evolution Algorithm. A total of six portfolios were constructed of which two were based on the CPT and the remaining on the GCA. The optimisation results of all constructed portfolios show that the GC-based portfolios outperformed the CPT-based portfolios. Specifically, GC Portfolio 4, comprising assets with higher CPT values in GC 1, emerged as the best-performing portfolio with a Sharpe ratio of 0.8497, significantly surpassing the highest Sharpe ratio among CPT-based portfolios (0.0206 for CPT Portfolio 2), further reinforcing the superiority of GCA in portfolio optimisation. The inclusion of the behavioural proxy in portfolio construction has a significant impact on adding value to investors' portfolios. Future research could explore refining asset selection by integrating machine learning techniques such as K-means clustering or reinforcement learning to enhance portfolio robustness. |
| format | Article |
| id | doaj-art-966e37c65f534e50b3aeb1963cbbc1df |
| institution | DOAJ |
| issn | 2332-2039 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Economics & Finance |
| spelling | doaj-art-966e37c65f534e50b3aeb1963cbbc1df2025-08-20T03:15:04ZengTaylor & Francis GroupCogent Economics & Finance2332-20392025-12-0113110.1080/23322039.2025.2494135Behavioural perspectives in forex portfolio value analysisKofi Agyarko Ababio0Necati Alp Erilli1Eric Nkansah2Jules Clement Mba3Department of Statistical Sciences, Kumasi Technical University, Kumasi, GhanaDepartment of Econometrics, Faculty of Economics and Administrative Sciences, Cumhuriyet University, Sivas, TurkeyDepartment of Banking and Finance, Kumasi Technical University, Kumasi, GhanaSchool of Economics, College of Business and Economics, Johannesburg University, Auckland Park Kingsway, Johannesburg, South AfricaThis paper combines Cumulative Prospect Theory (CPT) and the Grey Clustering Algorithm (GCA) to guide the optimization of forex portfolio selection. The United States Dollar (USD) against a universe of 84 other currencies was used for portfolio value analysis using the Differential Evolution Algorithm. A total of six portfolios were constructed of which two were based on the CPT and the remaining on the GCA. The optimisation results of all constructed portfolios show that the GC-based portfolios outperformed the CPT-based portfolios. Specifically, GC Portfolio 4, comprising assets with higher CPT values in GC 1, emerged as the best-performing portfolio with a Sharpe ratio of 0.8497, significantly surpassing the highest Sharpe ratio among CPT-based portfolios (0.0206 for CPT Portfolio 2), further reinforcing the superiority of GCA in portfolio optimisation. The inclusion of the behavioural proxy in portfolio construction has a significant impact on adding value to investors' portfolios. Future research could explore refining asset selection by integrating machine learning techniques such as K-means clustering or reinforcement learning to enhance portfolio robustness.https://www.tandfonline.com/doi/10.1080/23322039.2025.2494135Behavioural financecumulative prospect theorydecision-makingforexfinanceportfolio |
| spellingShingle | Kofi Agyarko Ababio Necati Alp Erilli Eric Nkansah Jules Clement Mba Behavioural perspectives in forex portfolio value analysis Cogent Economics & Finance Behavioural finance cumulative prospect theory decision-making forex finance portfolio |
| title | Behavioural perspectives in forex portfolio value analysis |
| title_full | Behavioural perspectives in forex portfolio value analysis |
| title_fullStr | Behavioural perspectives in forex portfolio value analysis |
| title_full_unstemmed | Behavioural perspectives in forex portfolio value analysis |
| title_short | Behavioural perspectives in forex portfolio value analysis |
| title_sort | behavioural perspectives in forex portfolio value analysis |
| topic | Behavioural finance cumulative prospect theory decision-making forex finance portfolio |
| url | https://www.tandfonline.com/doi/10.1080/23322039.2025.2494135 |
| work_keys_str_mv | AT kofiagyarkoababio behaviouralperspectivesinforexportfoliovalueanalysis AT necatialperilli behaviouralperspectivesinforexportfoliovalueanalysis AT ericnkansah behaviouralperspectivesinforexportfoliovalueanalysis AT julesclementmba behaviouralperspectivesinforexportfoliovalueanalysis |