A Novel Approach for Portfolio Optimization Using Fuzzy AHP Based on Gustafson Kessel Clustering Algorithm

Türkan Erbay Dalkılıç; Yeşim Akbaş; Serkan Akbaş

Makale detayları

A Novel Approach for Portfolio Optimization Using Fuzzy AHP Based on Gustafson Kessel Clustering Algorithm

Yazarlar:

Türkan Erbay Dalkılıç | Yeşim Akbaş | Serkan Akbaş

Yayıncı:

Burdur Mehmet Akif Ersoy University

Ana Başlıklar:

Operations Research

Dergi:

Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty Vol.11 Issue 4, pp.1436-1456

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001338059

pISSN:

2149-1658

Anahtar kelimeler:

Özet:

 Portfolio management involves modeling risk-return relationships. However, the diverse factors impacting financial markets introduce uncertainty into future portfolio selection. The aim of this study is to propose a portfolio selection model to assist investors in creating the most suitable investment plan in the financial market uncertainty. In this context, a preliminary reduction step is applied to the stocks using the Gustafson-Kessel GK algorithm, a fuzzy clustering method, to select portfolio stocks. Later, trapezoidal fuzzy numbers TrFNs were defined instead of triangular fuzzy numbers TFNs used in the Constrained Fuzzy Analytic Hierarchy Process AHP for portfolio selection problems. By using new fuzzy numbers, the weights of the criteria were obtained as TrFNs. Then, a linear programming problem was modeled using the weights of the obtained criteria as a TrFN. For this purpose, a method available in the literature was used that uses price variables in the objective function as TFNs. In this study, a linear programming model that uses these variables as TrFNs is proposed as an alternative to the method that uses the price variables in the objective function as TFNs. In this proposed model, the weights obtained from the Constrained Fuzzy AHP using TrFNs are used as price variables in the objective function of the created linear programming problem. Proposed model then applied to the 48-month return data set of stocks in the Istanbul Stock Exchange 100 ISE-100 index to determine which stocks the investor should choose and the investment rates investor should make in these stocks. In addition, in order to examine the effectiveness of the proposed model within the scope of the study, portfolio distributions were obtained with different portfolio optimization methods using the same data set and the results were compared.

 Portfolio management involves modeling risk-return relationships. However, the diverse factors impacting financial markets introduce uncertainty into future portfolio selection. The aim of this study is to propose a portfolio selection model to assist investors in creating the most suitable investment plan in the financial market uncertainty. In this context, a preliminary reduction step is applied to the stocks using the Gustafson-Kessel GK algorithm, a fuzzy clustering method, to select portfolio stocks. Later, trapezoidal fuzzy numbers TrFNs were defined instead of triangular fuzzy numbers TFNs used in the Constrained Fuzzy Analytic Hierarchy Process AHP for portfolio selection problems. By using new fuzzy numbers, the weights of the criteria were obtained as TrFNs. Then, a linear programming problem was modeled using the weights of the obtained criteria as a TrFN. For this purpose, a method available in the literature was used that uses price variables in the objective function as TFNs. In this study, a linear programming model that uses these variables as TrFNs is proposed as an alternative to the method that uses the price variables in the objective function as TFNs. In this proposed model, the weights obtained from the Constrained Fuzzy AHP using TrFNs are used as price variables in the objective function of the created linear programming problem. Proposed model then applied to the 48-month return data set of stocks in the Istanbul Stock Exchange 100 ISE-100 index to determine which stocks the investor should choose and the investment rates investor should make in these stocks. In addition, in order to examine the effectiveness of the proposed model within the scope of the study, portfolio distributions were obtained with different portfolio optimization methods using the same data set and the results were compared.

 Portfolio management involves modeling risk-return relationships. However, the diverse factors impacting financial markets introduce uncertainty into future portfolio selection. The aim of this study is to propose a portfolio selection model to assist investors in creating the most suitable investment plan in the financial market uncertainty. In this context, a preliminary reduction step is applied to the stocks using the Gustafson-Kessel GK algorithm, a fuzzy clustering method, to select portfolio stocks. Later, trapezoidal fuzzy numbers TrFNs were defined instead of triangular fuzzy numbers TFNs used in the Constrained Fuzzy Analytic Hierarchy Process AHP for portfolio selection problems. By using new fuzzy numbers, the weights of the criteria were obtained as TrFNs. Then, a linear programming problem was modeled using the weights of the obtained criteria as a TrFN. For this purpose, a method available in the literature was used that uses price variables in the objective function as TFNs. In this study, a linear programming model that uses these variables as TrFNs is proposed as an alternative to the method that uses the price variables in the objective function as TFNs. In this proposed model, the weights obtained from the Constrained Fuzzy AHP using TrFNs are used as price variables in the objective function of the created linear programming problem. Proposed model then applied to the 48-month return data set of stocks in the Istanbul Stock Exchange 100 ISE-100 index to determine which stocks the investor should choose and the investment rates investor should make in these stocks. In addition, in order to examine the effectiveness of the proposed model within the scope of the study, portfolio distributions were obtained with different portfolio optimization methods using the same data set and the results were compared.

 Portfolio management involves modeling risk-return relationships. However, the diverse factors impacting financial markets introduce uncertainty into future portfolio selection. The aim of this study is to propose a portfolio selection model to assist investors in creating the most suitable investment plan in the financial market uncertainty. In this context, a preliminary reduction step is applied to the stocks using the Gustafson-Kessel GK algorithm, a fuzzy clustering method, to select portfolio stocks. Later, trapezoidal fuzzy numbers TrFNs were defined instead of triangular fuzzy numbers TFNs used in the Constrained Fuzzy Analytic Hierarchy Process AHP for portfolio selection problems. By using new fuzzy numbers, the weights of the criteria were obtained as TrFNs. Then, a linear programming problem was modeled using the weights of the obtained criteria as a TrFN. For this purpose, a method available in the literature was used that uses price variables in the objective function as TFNs. In this study, a linear programming model that uses these variables as TrFNs is proposed as an alternative to the method that uses the price variables in the objective function as TFNs. In this proposed model, the weights obtained from the Constrained Fuzzy AHP using TrFNs are used as price variables in the objective function of the created linear programming problem. Proposed model then applied to the 48-month return data set of stocks in the Istanbul Stock Exchange 100 ISE-100 index to determine which stocks the investor should choose and the investment rates investor should make in these stocks. In addition, in order to examine the effectiveness of the proposed model within the scope of the study, portfolio distributions were obtained with different portfolio optimization methods using the same data set and the results were compared.

Makale detayları

A Novel Approach for Portfolio Optimization Using Fuzzy AHP Based on Gustafson Kessel Clustering Algorithm

Yazarlar:

Türkan Erbay Dalkılıç | Yeşim Akbaş | Serkan Akbaş

Yayıncı:

Burdur Mehmet Akif Ersoy University

Ana Başlıklar:

Operations Research

Dergi:

Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty Vol.11 Issue 4, pp.1436-1456

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001338059

pISSN:

2149-1658

Fuzzy AHP

Gustafson-Kessel algorithm

Portfolio selection

Trapezoidal fuzzy numbersFuzzy AHP

Gustafson-Kessel algorithm

Portfolio selection

Trapezoidal fuzzy numbers

Sayı

Makale detayları

Yazarlar: Türkan Erbay Dalkılıç | Yeşim Akbaş | Serkan Akbaş

Yayıncı: Burdur Mehmet Akif Ersoy University

Ana Başlıklar: Operations Research

Dergi: Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty Vol.11 Issue 4, pp.1436-1456

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001338059

pISSN: 2149-1658

Sayı

Yazarlar:

Türkan Erbay Dalkılıç | Yeşim Akbaş | Serkan Akbaş

Yayıncı:

Burdur Mehmet Akif Ersoy University

Ana Başlıklar:

Operations Research

Dergi:

Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty Vol.11 Issue 4, pp.1436-1456

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001338059

pISSN:

2149-1658