Fuzzy portfolio selection using genetic algorithm
Abstract This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also e...
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| Autor*in: |
Abiyev, Rahib H. [verfasserIn] Menekay, Mustafa [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2007 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 11(2007), 12 vom: 20. März, Seite 1157-1163 |
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| Übergeordnetes Werk: |
volume:11 ; year:2007 ; number:12 ; day:20 ; month:03 ; pages:1157-1163 |
| Links: |
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| DOI / URN: |
10.1007/s00500-007-0157-z |
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| 520 | |a Abstract This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method. | ||
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10.1007/s00500-007-0157-z doi (DE-627)SPR006473199 (SPR)s00500-007-0157-z-e DE-627 ger DE-627 rakwb eng Abiyev, Rahib H. verfasserin aut Fuzzy portfolio selection using genetic algorithm 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method. Fuzzy portfolio selection (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Portfolio optimization (dpeaa)DE-He213 Menekay, Mustafa verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 11(2007), 12 vom: 20. März, Seite 1157-1163 (DE-627)SPR006469531 nnns volume:11 year:2007 number:12 day:20 month:03 pages:1157-1163 https://dx.doi.org/10.1007/s00500-007-0157-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 11 2007 12 20 03 1157-1163 |
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10.1007/s00500-007-0157-z doi (DE-627)SPR006473199 (SPR)s00500-007-0157-z-e DE-627 ger DE-627 rakwb eng Abiyev, Rahib H. verfasserin aut Fuzzy portfolio selection using genetic algorithm 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method. Fuzzy portfolio selection (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Portfolio optimization (dpeaa)DE-He213 Menekay, Mustafa verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 11(2007), 12 vom: 20. März, Seite 1157-1163 (DE-627)SPR006469531 nnns volume:11 year:2007 number:12 day:20 month:03 pages:1157-1163 https://dx.doi.org/10.1007/s00500-007-0157-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 11 2007 12 20 03 1157-1163 |
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10.1007/s00500-007-0157-z doi (DE-627)SPR006473199 (SPR)s00500-007-0157-z-e DE-627 ger DE-627 rakwb eng Abiyev, Rahib H. verfasserin aut Fuzzy portfolio selection using genetic algorithm 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method. Fuzzy portfolio selection (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Portfolio optimization (dpeaa)DE-He213 Menekay, Mustafa verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 11(2007), 12 vom: 20. März, Seite 1157-1163 (DE-627)SPR006469531 nnns volume:11 year:2007 number:12 day:20 month:03 pages:1157-1163 https://dx.doi.org/10.1007/s00500-007-0157-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 11 2007 12 20 03 1157-1163 |
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10.1007/s00500-007-0157-z doi (DE-627)SPR006473199 (SPR)s00500-007-0157-z-e DE-627 ger DE-627 rakwb eng Abiyev, Rahib H. verfasserin aut Fuzzy portfolio selection using genetic algorithm 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method. Fuzzy portfolio selection (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Portfolio optimization (dpeaa)DE-He213 Menekay, Mustafa verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 11(2007), 12 vom: 20. März, Seite 1157-1163 (DE-627)SPR006469531 nnns volume:11 year:2007 number:12 day:20 month:03 pages:1157-1163 https://dx.doi.org/10.1007/s00500-007-0157-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 11 2007 12 20 03 1157-1163 |
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10.1007/s00500-007-0157-z doi (DE-627)SPR006473199 (SPR)s00500-007-0157-z-e DE-627 ger DE-627 rakwb eng Abiyev, Rahib H. verfasserin aut Fuzzy portfolio selection using genetic algorithm 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method. Fuzzy portfolio selection (dpeaa)DE-He213 Genetic algorithm (dpeaa)DE-He213 Portfolio optimization (dpeaa)DE-He213 Menekay, Mustafa verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 11(2007), 12 vom: 20. März, Seite 1157-1163 (DE-627)SPR006469531 nnns volume:11 year:2007 number:12 day:20 month:03 pages:1157-1163 https://dx.doi.org/10.1007/s00500-007-0157-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 11 2007 12 20 03 1157-1163 |
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Abstract This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method. |
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Abstract This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method. |
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Abstract This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR006473199</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002716.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2007 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-007-0157-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006473199</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-007-0157-z-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Abiyev, Rahib H.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fuzzy portfolio selection using genetic algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2007</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy portfolio selection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Genetic algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Portfolio optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Menekay, Mustafa</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">11(2007), 12 vom: 20. März, Seite 1157-1163</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2007</subfield><subfield code="g">number:12</subfield><subfield code="g">day:20</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:1157-1163</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-007-0157-z</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2007</subfield><subfield code="e">12</subfield><subfield code="b">20</subfield><subfield code="c">03</subfield><subfield code="h">1157-1163</subfield></datafield></record></collection>
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