Araştırma Makalesi
BibTex RIS Kaynak Göster

A Bibliometric Analysis of Possibilistic Portfolio Selection Models

Yıl 2024, , 127 - 141, 30.06.2024
https://doi.org/10.47097/piar.1426345

Öz

Possibility theory is one of the most used uncertainty theories in decision-making. This study aims to examine possibilistic portfolio selection models. In this context, we perform their bibliometric analysis with the Web of Science (WOS) data, using the Bibliometrix, without limiting the timespan. We get many results by analyzing the data of 303 documents, of which timespan is from 1995 to 2023. We see that W. G. Zhang is the most influential author in this field. The paper introducing the possibilistic mean-variance (MV) model is the most influential document in this field. The paper introducing Markowitz’s MV model is the most influential reference. China is the most productive country in this field, whereas The South China University of Technology is the most productive institution in this field. Fuzzy Sets and Systems is the most influential journal in this field. Variance originated from Markowitz’s MV model is the most critical keyword plus in this field. It has also maintained its trend topic position for a long time. To the best of our knowledge, this is the first paper making a bibliometric analysis of possibilistic portfolio selection models.

Kaynakça

  • Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.
  • Batra, S., Saini, M., Yadav, M., & Aggarwal, V. (2022). Mapping the intellectual structure and demystifying the research trend of cross listing: A bibliometric analysis. Managerial Finance, 49(6), 992-1016.
  • Carlsson, C., & Fuller, R. (2001). On possibilistic mean value and variance of fuzzy numbers. Fuzzy Sets and Systems, 122(2), 315-326.
  • Carlsson, C., Fuller, R., & Majlender, P. (2002). A possibilistic approach to selecting portfolios with highest utility score. Fuzzy Sets and Systems, 131(1), 13-21.
  • Chen, W. (2009). Weighted portfolio selection models based on possibility theory. Fuzzy Information and Engineering, 1, 115-127.
  • Cobo, M. J., Jürgens, B., Herrero-Solana, V., Martínez, M. A., & Herrera-Viedma, E. (2018). Industry 4.0: A perspective based on bibliometric analysis. Procedia Computer Science, 139, 364-371.
  • Deng, X., & Li, R. (2012). A portfolio selection model with borrowing constraint based on possibility theory. Applied Soft Computing, 12(2), 754-758.
  • Deng, X., & Lin, Y. (2022). Improved particle swarm optimization for mean-variance-Yager entropy-social responsibility portfolio with complex reality constraints. Engineering Computations, 39(4), 1288-1316.
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296.
  • Dubois, D. (2006). Possibility theory and statistical reasoning. Computational Statistics & Data Analysis, 51(1), 47-69.
  • Eskorouchi, A., Ghanbari, H., & Mohammadi, E. (in press). A scientometric analysis of robust portfolio optimization. Iranian Journal of Accounting, Auditing and Finance.
  • Gallucci, C., Santulli, R., & Lagasio, V. (2022). The conceptualization of environmental, social and governance risks in portfolio studies: A systematic literature review. Socio-Economic Planning Sciences, 84, 101382.
  • Ghanbari, H., Safari, M., Ghousi, R., Mohammadi, E., & Nakharutai, N. (2023). Bibliometric analysis of risk measures for portfolio optimization. Accounting, 9(2), 95-108.
  • Göktaş, F. (2023). Ortogonal olabilirlik ortalama-varyans modeli. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(Ek Sayı), 29-41.
  • Göktaş, F., & Duran, A. (2019). A new possibilistic mean-variance model based on the principal components analysis: an application on the Turkish holding stocks. Journal of Multiple Valued Logic & Soft Computing, 32(5-6). 455-476.
  • Göktaş, F., & Duran, A. (2020). New robust portfolio selection models based on the principal components analysis: An application on the Turkish holding stocks. Journal of Multiple-Valued Logic & Soft Computing, 34(1-2), 43-58.
  • Göktaş, F. ve Güçlü, F. (2024). Yeni bir çok kriterli karar verme yaklaşımı “olabilirlik değerlendirme sistemi”: Katılım fonları üzerine bir uygulama. Black Sea Journal of Engineering and Science, 7(1), 1-8.
  • Gunjan, A., & Bhattacharyya, S. (2023). A brief review of portfolio optimization techniques. Artificial Intelligence Review, 56(5), 3847-3886.
  • Gupta, S., Walia, N., Singh, S., & Gupta, S. (2023). A systematic literature review and bibliometric analysis of noise trading. Qualitative Research in Financial Markets, 15(1), 190-215.
  • Hu, J., Sui, Y., & Ma, F. (2021). A portfolio selection model based on the interval number. Mathematical Problems in Engineering, 2577264.
  • Inuiguchi, M., & Ramık, J. (2000). Possibilistic linear programming: A brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets and Systems, 111(1), 3-28.
  • Inuiguchi, M., & Tanino, T. (2000). Portfolio selection under independent possibilistic information. Fuzzy Sets and Systems, 115(1), 83-92.
  • Li, X., Guo, S., & Yu, L. (2015). Skewness of fuzzy numbers and its applications in portfolio selection. IEEE Transactions on Fuzzy Systems, 23(6), 2135-2143.
  • Liu, Y. J., & Zhang, W. G. (2015). A multi-period fuzzy portfolio optimization model with minimum transaction lots. European Journal of Operational Research, 242(3), 933-941.
  • Liu, Y. J., & Zhang, W. G. (2019). Possibilistic moment models for multi-period portfolio selection with fuzzy returns. Computational Economics, 53, 1657-1686.
  • Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
  • Markowitz, H. (1959). Portfolio selection. John Wiley.
  • Marzuki, A., Basah, M. Y. A., Nor, F. M., Ramli, N. A., & Ab Aziz, M. R. (2023). The influence of ESG, SRI, ethical, and impact investing activities on portfolio and financial performance - Bibliometric analysis/mapping and clustering analysis. Journal of Risk Financial Management, 16(7), 321.
  • Milhomem, D. A., & Dantas, M. J. P. (2020). Analysis of new approaches used in portfolio optimization: A systematic literature review. Production, 30, e20190144.
  • Mundi, H. S., & Kumar, D. (2023). The potential of alternative investments as an asset class: a thematic and bibliometric review. Qualitative Research in Financial Markets, 15(1), 119-141.
  • Pedersen, L. H., Fitzgibbons, S., & Pomorski, L. (2021). Responsible investing: The ESG-efficient frontier. Journal of Financial Economics, 142(2), 572-597.
  • Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications, 9(1), 12.
  • Singhania, M., Bhan, I., & Chadha, G. (2023). Sustainable investments: A scientometric review and research agenda. Managerial Finance, 50(1), 266-294.
  • Souliotis, G., Alanazi, Y., & Papadopoulos, B. (2022). Construction of fuzzy numbers via cumulative distribution function. Mathematics, 10(18), 3350.
  • Tanaka, H., & Guo, P. (1999). Portfolio selection based on upper and lower exponential possibility distributions. European Journal of Operational Research, 114(1), 115-126.
  • Tanaka, H., Guo, P., & Türksen, I. B. (2000). Portfolio selection based on fuzzy probabilities and possibility distributions. Fuzzy Sets and Systems, 111(3), 387-397.
  • Tanaka, H., Nakayama, H., & Yanagimoto, A. (1995). Possibility portfolio selection. In Proceedings of IEEE International Conference on Fuzzy Systems, 2, 813-818.
  • Taş, O., Kahraman, C., & Güran, C. B. (2016). A scenario based linear fuzzy approach in portfolio selection problem: application in the Istanbul Stock Exchange. Journal of Multiple Valued Logic & Soft Computing, 26(3-5), 269-294.
  • Tavakol, M., & Wetzel, A. (2020). Factor analysis: A means for theory and instrument development in support of construct validity. International Journal of Medical Education, 11, 245.
  • Vercher, E., Bermúdez, J. D., & Segura, J. V. (2007). Fuzzy portfolio optimization under downside risk measures. Fuzzy Sets and Systems, 158(7), 769-782.
  • Watada, J. (1997). Fuzzy portfolio selection and its applications to decision making. Tatra Mountains Mathematical Publication, 13, 219-248.
  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
  • Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1), 3-28.
  • Zaimovic, A., Omanovic, A., & Arnaut-Berilo, A. (2021). How many stocks are sufficient for equity portfolio diversification? A review of the literature. Journal of Risk and Financial Management, 14(11), 551.
  • Zhang, P., & Zhang, W. G. (2014). Multiperiod mean absolute deviation fuzzy portfolio selection model with risk control and cardinality constraints. Fuzzy Sets and Systems, 255, 74-91.
  • Zhang, W. G. (2007). Possibilistic mean–standard deviation models to portfolio selection for bounded assets. Applied Mathematics and Computation, 189(2), 1614-1623.
  • Zhang, W. G., Liu, Y. J., & Xu, W. J. (2012). A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs. European Journal of Operational Research, 222(2), 341-349.
  • Zhang, W. G., & Xiao, W. L. (2009). On weighted lower and upper possibilistic means and variances of fuzzy numbers and its application in decision. Knowledge and Information Systems, 18, 311-330.
  • Zhang, W. G., Wang, Y. L., Chen, Z. P., & Nie, Z. K. (2007). Possibilistic mean–variance models and efficient frontiers for portfolio selection problem. Information Sciences, 177(13), 2787-2801.
  • Zhang, W. G., Zhang, X. L., & Xiao, W. L. (2009). Portfolio selection under possibilistic mean–variance utility and a SMO algorithm. European Journal of Operational Research, 197(2), 693-700.
  • Zhang, Y., Li, X., & Guo, S. (2018). Portfolio selection problems with Markowitz’s mean–variance framework: A review of literature. Fuzzy Optimization and Decision Making, 17, 125-158.
  • Zhou, W., Gu, Q., & Yu, D. (2020). Knowledge framework and evolution of fuzzy portfolio research: A bibliometric analysis. Mathematical Problems in Engineering, 3067461.

Olabilirlik Portföy Seçim Modellerinin Bibliyometrik Analizi

Yıl 2024, , 127 - 141, 30.06.2024
https://doi.org/10.47097/piar.1426345

Öz

Olabilirlik teorisi karar vermede en çok kullanılan belirsizlik teorilerinden biridir. Bu çalışma olabilirlik portföy seçim modellerini incelemeyi amaçlamaktadır. Bu bağlamda bunların bibliyometrik analizi, zaman sınırlaması olmadan, Web of Science (WOS) verileriyle, Bibliometrix kullanılarak yapılmıştır. Zaman aralığı 1995'ten 2023'e kadar olan 303 belgenin verileri analiz edilerek birçok sonuca ulaşılmıştır. Bu alandaki en etkili yazarın W. G. Zhang olduğu görülmüştür. Olabilirlik ortalama-varyans (OV) modelinin tanıtıldığı makale bu alandaki en etkili belgedir. Markowitz'in OV modelinin tanıtıldığı makale en etkili referanstır. Çin bu alandaki en üretken ülke iken, The South China University of Technology bu alandaki en üretken kurumdur. Fuzzy Sets and Systems bu alandaki en etkili dergidir. Markowitz'in OV modeli kökenli olan varyans, bu alandaki en kritik anahtar kelime “plus” olarak bulunmuştur. Varyans aynı zamanda trend konu konumunu uzun süre korumuştur. Bildiğimiz kadarıyla bu çalışma, olabilirlik portföy seçimi modellerinin bibliyometrik analizini yapan ilk çalışmadır.

Kaynakça

  • Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.
  • Batra, S., Saini, M., Yadav, M., & Aggarwal, V. (2022). Mapping the intellectual structure and demystifying the research trend of cross listing: A bibliometric analysis. Managerial Finance, 49(6), 992-1016.
  • Carlsson, C., & Fuller, R. (2001). On possibilistic mean value and variance of fuzzy numbers. Fuzzy Sets and Systems, 122(2), 315-326.
  • Carlsson, C., Fuller, R., & Majlender, P. (2002). A possibilistic approach to selecting portfolios with highest utility score. Fuzzy Sets and Systems, 131(1), 13-21.
  • Chen, W. (2009). Weighted portfolio selection models based on possibility theory. Fuzzy Information and Engineering, 1, 115-127.
  • Cobo, M. J., Jürgens, B., Herrero-Solana, V., Martínez, M. A., & Herrera-Viedma, E. (2018). Industry 4.0: A perspective based on bibliometric analysis. Procedia Computer Science, 139, 364-371.
  • Deng, X., & Li, R. (2012). A portfolio selection model with borrowing constraint based on possibility theory. Applied Soft Computing, 12(2), 754-758.
  • Deng, X., & Lin, Y. (2022). Improved particle swarm optimization for mean-variance-Yager entropy-social responsibility portfolio with complex reality constraints. Engineering Computations, 39(4), 1288-1316.
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296.
  • Dubois, D. (2006). Possibility theory and statistical reasoning. Computational Statistics & Data Analysis, 51(1), 47-69.
  • Eskorouchi, A., Ghanbari, H., & Mohammadi, E. (in press). A scientometric analysis of robust portfolio optimization. Iranian Journal of Accounting, Auditing and Finance.
  • Gallucci, C., Santulli, R., & Lagasio, V. (2022). The conceptualization of environmental, social and governance risks in portfolio studies: A systematic literature review. Socio-Economic Planning Sciences, 84, 101382.
  • Ghanbari, H., Safari, M., Ghousi, R., Mohammadi, E., & Nakharutai, N. (2023). Bibliometric analysis of risk measures for portfolio optimization. Accounting, 9(2), 95-108.
  • Göktaş, F. (2023). Ortogonal olabilirlik ortalama-varyans modeli. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(Ek Sayı), 29-41.
  • Göktaş, F., & Duran, A. (2019). A new possibilistic mean-variance model based on the principal components analysis: an application on the Turkish holding stocks. Journal of Multiple Valued Logic & Soft Computing, 32(5-6). 455-476.
  • Göktaş, F., & Duran, A. (2020). New robust portfolio selection models based on the principal components analysis: An application on the Turkish holding stocks. Journal of Multiple-Valued Logic & Soft Computing, 34(1-2), 43-58.
  • Göktaş, F. ve Güçlü, F. (2024). Yeni bir çok kriterli karar verme yaklaşımı “olabilirlik değerlendirme sistemi”: Katılım fonları üzerine bir uygulama. Black Sea Journal of Engineering and Science, 7(1), 1-8.
  • Gunjan, A., & Bhattacharyya, S. (2023). A brief review of portfolio optimization techniques. Artificial Intelligence Review, 56(5), 3847-3886.
  • Gupta, S., Walia, N., Singh, S., & Gupta, S. (2023). A systematic literature review and bibliometric analysis of noise trading. Qualitative Research in Financial Markets, 15(1), 190-215.
  • Hu, J., Sui, Y., & Ma, F. (2021). A portfolio selection model based on the interval number. Mathematical Problems in Engineering, 2577264.
  • Inuiguchi, M., & Ramık, J. (2000). Possibilistic linear programming: A brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets and Systems, 111(1), 3-28.
  • Inuiguchi, M., & Tanino, T. (2000). Portfolio selection under independent possibilistic information. Fuzzy Sets and Systems, 115(1), 83-92.
  • Li, X., Guo, S., & Yu, L. (2015). Skewness of fuzzy numbers and its applications in portfolio selection. IEEE Transactions on Fuzzy Systems, 23(6), 2135-2143.
  • Liu, Y. J., & Zhang, W. G. (2015). A multi-period fuzzy portfolio optimization model with minimum transaction lots. European Journal of Operational Research, 242(3), 933-941.
  • Liu, Y. J., & Zhang, W. G. (2019). Possibilistic moment models for multi-period portfolio selection with fuzzy returns. Computational Economics, 53, 1657-1686.
  • Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
  • Markowitz, H. (1959). Portfolio selection. John Wiley.
  • Marzuki, A., Basah, M. Y. A., Nor, F. M., Ramli, N. A., & Ab Aziz, M. R. (2023). The influence of ESG, SRI, ethical, and impact investing activities on portfolio and financial performance - Bibliometric analysis/mapping and clustering analysis. Journal of Risk Financial Management, 16(7), 321.
  • Milhomem, D. A., & Dantas, M. J. P. (2020). Analysis of new approaches used in portfolio optimization: A systematic literature review. Production, 30, e20190144.
  • Mundi, H. S., & Kumar, D. (2023). The potential of alternative investments as an asset class: a thematic and bibliometric review. Qualitative Research in Financial Markets, 15(1), 119-141.
  • Pedersen, L. H., Fitzgibbons, S., & Pomorski, L. (2021). Responsible investing: The ESG-efficient frontier. Journal of Financial Economics, 142(2), 572-597.
  • Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications, 9(1), 12.
  • Singhania, M., Bhan, I., & Chadha, G. (2023). Sustainable investments: A scientometric review and research agenda. Managerial Finance, 50(1), 266-294.
  • Souliotis, G., Alanazi, Y., & Papadopoulos, B. (2022). Construction of fuzzy numbers via cumulative distribution function. Mathematics, 10(18), 3350.
  • Tanaka, H., & Guo, P. (1999). Portfolio selection based on upper and lower exponential possibility distributions. European Journal of Operational Research, 114(1), 115-126.
  • Tanaka, H., Guo, P., & Türksen, I. B. (2000). Portfolio selection based on fuzzy probabilities and possibility distributions. Fuzzy Sets and Systems, 111(3), 387-397.
  • Tanaka, H., Nakayama, H., & Yanagimoto, A. (1995). Possibility portfolio selection. In Proceedings of IEEE International Conference on Fuzzy Systems, 2, 813-818.
  • Taş, O., Kahraman, C., & Güran, C. B. (2016). A scenario based linear fuzzy approach in portfolio selection problem: application in the Istanbul Stock Exchange. Journal of Multiple Valued Logic & Soft Computing, 26(3-5), 269-294.
  • Tavakol, M., & Wetzel, A. (2020). Factor analysis: A means for theory and instrument development in support of construct validity. International Journal of Medical Education, 11, 245.
  • Vercher, E., Bermúdez, J. D., & Segura, J. V. (2007). Fuzzy portfolio optimization under downside risk measures. Fuzzy Sets and Systems, 158(7), 769-782.
  • Watada, J. (1997). Fuzzy portfolio selection and its applications to decision making. Tatra Mountains Mathematical Publication, 13, 219-248.
  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
  • Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1), 3-28.
  • Zaimovic, A., Omanovic, A., & Arnaut-Berilo, A. (2021). How many stocks are sufficient for equity portfolio diversification? A review of the literature. Journal of Risk and Financial Management, 14(11), 551.
  • Zhang, P., & Zhang, W. G. (2014). Multiperiod mean absolute deviation fuzzy portfolio selection model with risk control and cardinality constraints. Fuzzy Sets and Systems, 255, 74-91.
  • Zhang, W. G. (2007). Possibilistic mean–standard deviation models to portfolio selection for bounded assets. Applied Mathematics and Computation, 189(2), 1614-1623.
  • Zhang, W. G., Liu, Y. J., & Xu, W. J. (2012). A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs. European Journal of Operational Research, 222(2), 341-349.
  • Zhang, W. G., & Xiao, W. L. (2009). On weighted lower and upper possibilistic means and variances of fuzzy numbers and its application in decision. Knowledge and Information Systems, 18, 311-330.
  • Zhang, W. G., Wang, Y. L., Chen, Z. P., & Nie, Z. K. (2007). Possibilistic mean–variance models and efficient frontiers for portfolio selection problem. Information Sciences, 177(13), 2787-2801.
  • Zhang, W. G., Zhang, X. L., & Xiao, W. L. (2009). Portfolio selection under possibilistic mean–variance utility and a SMO algorithm. European Journal of Operational Research, 197(2), 693-700.
  • Zhang, Y., Li, X., & Guo, S. (2018). Portfolio selection problems with Markowitz’s mean–variance framework: A review of literature. Fuzzy Optimization and Decision Making, 17, 125-158.
  • Zhou, W., Gu, Q., & Yu, D. (2020). Knowledge framework and evolution of fuzzy portfolio research: A bibliometric analysis. Mathematical Problems in Engineering, 3067461.
Toplam 52 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yöneylem Araştırması, Finans
Bölüm Araştırma Makaleleri
Yazarlar

Furkan Göktaş 0000-0001-9291-3912

Yayımlanma Tarihi 30 Haziran 2024
Gönderilme Tarihi 26 Ocak 2024
Kabul Tarihi 11 Mayıs 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Göktaş, F. (2024). A Bibliometric Analysis of Possibilistic Portfolio Selection Models. Pamukkale Üniversitesi İşletme Araştırmaları Dergisi, 11(1), 127-141. https://doi.org/10.47097/piar.1426345

Pamukkale Üniversitesi İşletme Araştırmaları Dergisinde yayınlanmış makalelerin telif hakları Creative Commons Atıf-Gayriticari 4.0 Uluslararası Lisansı (CC BY-NC-ND 4.0) kapsamındadır.

by-nc-nd.png