A Hybrid Genetic Algorithm–analytic Hierarchy Process Approach to Optimal Portfolio Selection: Evidence from the Amman Stock Exchange

Said Sami Al Hallaq; Mohammad Ajlouni; Anas Ghazalt; Mahmoud Al-Saadi

doi:doi:10.11648/j.ib.20260101.14

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A Hybrid Genetic Algorithm–analytic Hierarchy Process Approach to Optimal Portfolio Selection: Evidence from the Amman Stock Exchange

Said Sami Al Hallaq^*

, Mohammad Ajlouni, Anas Ghazalt

, Mahmoud Al-Saadi

Published in Innovation Business (Volume 1, Issue 1)

Received: 27 October 2025 Accepted: 26 January 2026 Published: 24 February 2026

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Abstract

This study investigates the effectiveness of a hybrid decision-support framework that integrates a Hybrid Genetic Algorithm (HGA) with the Analytic Hierarchy Process (AHP) to identify optimal investment portfolios in the Amman Stock Exchange (ASE). Using daily stock return data for companies listed on the ASE over the period January 1, 2015 to December 31, 2015, the HGA is employed to generate and evolve 10,000 candidate portfolios, each consisting of six stocks. The generated portfolios are evaluated based on the risk–return trade-off, and the efficient frontier is constructed. Subsequently, the AHP is applied to rank the efficient portfolios according to seven evaluation criteria: expected return, risk, beta, liquidity, Sharpe ratio, Treynor ratio, and Jensen’s alpha. The results demonstrate that the proposed HGA–AHP framework effectively identifies portfolios located on the efficient frontier and selects an optimal portfolio that exhibits superior risk-adjusted performance relative to the market benchmark. The hybrid approach successfully addresses nonlinear constraints and discrete asset selection issues that limit traditional optimization techniques. This study contributes empirical evidence from an emerging market and provides a replicable hybrid portfolio optimization framework for investors, portfolio managers, and financial institutions.

Published in	Innovation Business (Volume 1, Issue 1)
DOI	10.11648/j.ib.20260101.14
Page(s)	37-54
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Hybrid Genetic Algorithm, Analytic Hierarchy Process, Portfolio Optimization, Efficient Frontier, Amman Stock Exchange, Investment Decision-making

References

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APA Style

Hallaq, S. S. A., Ajlouni, M., Ghazalt, A., Al-Saadi, M. (2026). A Hybrid Genetic Algorithm–analytic Hierarchy Process Approach to Optimal Portfolio Selection: Evidence from the Amman Stock Exchange. Innovation Business, 1(1), 37-54. https://doi.org/10.11648/j.ib.20260101.14

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ACS Style

Hallaq, S. S. A.; Ajlouni, M.; Ghazalt, A.; Al-Saadi, M. A Hybrid Genetic Algorithm–analytic Hierarchy Process Approach to Optimal Portfolio Selection: Evidence from the Amman Stock Exchange. Innov. Bus. 2026, 1(1), 37-54. doi: 10.11648/j.ib.20260101.14

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AMA Style

Hallaq SSA, Ajlouni M, Ghazalt A, Al-Saadi M. A Hybrid Genetic Algorithm–analytic Hierarchy Process Approach to Optimal Portfolio Selection: Evidence from the Amman Stock Exchange. Innov Bus. 2026;1(1):37-54. doi: 10.11648/j.ib.20260101.14

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@article{10.11648/j.ib.20260101.14,
  author = {Said Sami Al Hallaq and Mohammad Ajlouni and Anas Ghazalt and Mahmoud Al-Saadi},
  title = {A Hybrid Genetic Algorithm–analytic Hierarchy Process Approach to Optimal Portfolio Selection: Evidence from the Amman Stock Exchange},
  journal = {Innovation Business},
  volume = {1},
  number = {1},
  pages = {37-54},
  doi = {10.11648/j.ib.20260101.14},
  url = {https://doi.org/10.11648/j.ib.20260101.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ib.20260101.14},
  abstract = {This study investigates the effectiveness of a hybrid decision-support framework that integrates a Hybrid Genetic Algorithm (HGA) with the Analytic Hierarchy Process (AHP) to identify optimal investment portfolios in the Amman Stock Exchange (ASE). Using daily stock return data for companies listed on the ASE over the period January 1, 2015 to December 31, 2015, the HGA is employed to generate and evolve 10,000 candidate portfolios, each consisting of six stocks. The generated portfolios are evaluated based on the risk–return trade-off, and the efficient frontier is constructed. Subsequently, the AHP is applied to rank the efficient portfolios according to seven evaluation criteria: expected return, risk, beta, liquidity, Sharpe ratio, Treynor ratio, and Jensen’s alpha. The results demonstrate that the proposed HGA–AHP framework effectively identifies portfolios located on the efficient frontier and selects an optimal portfolio that exhibits superior risk-adjusted performance relative to the market benchmark. The hybrid approach successfully addresses nonlinear constraints and discrete asset selection issues that limit traditional optimization techniques. This study contributes empirical evidence from an emerging market and provides a replicable hybrid portfolio optimization framework for investors, portfolio managers, and financial institutions.},
 year = {2026}
}

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TY  - JOUR
T1  - A Hybrid Genetic Algorithm–analytic Hierarchy Process Approach to Optimal Portfolio Selection: Evidence from the Amman Stock Exchange
AU  - Said Sami Al Hallaq
AU  - Mohammad Ajlouni
AU  - Anas Ghazalt
AU  - Mahmoud Al-Saadi
Y1  - 2026/02/24
PY  - 2026
N1  - https://doi.org/10.11648/j.ib.20260101.14
DO  - 10.11648/j.ib.20260101.14
T2  - Innovation Business
JF  - Innovation Business
JO  - Innovation Business
SP  - 37
EP  - 54
PB  - Science Publishing Group
UR  - https://doi.org/10.11648/j.ib.20260101.14
AB  - This study investigates the effectiveness of a hybrid decision-support framework that integrates a Hybrid Genetic Algorithm (HGA) with the Analytic Hierarchy Process (AHP) to identify optimal investment portfolios in the Amman Stock Exchange (ASE). Using daily stock return data for companies listed on the ASE over the period January 1, 2015 to December 31, 2015, the HGA is employed to generate and evolve 10,000 candidate portfolios, each consisting of six stocks. The generated portfolios are evaluated based on the risk–return trade-off, and the efficient frontier is constructed. Subsequently, the AHP is applied to rank the efficient portfolios according to seven evaluation criteria: expected return, risk, beta, liquidity, Sharpe ratio, Treynor ratio, and Jensen’s alpha. The results demonstrate that the proposed HGA–AHP framework effectively identifies portfolios located on the efficient frontier and selects an optimal portfolio that exhibits superior risk-adjusted performance relative to the market benchmark. The hybrid approach successfully addresses nonlinear constraints and discrete asset selection issues that limit traditional optimization techniques. This study contributes empirical evidence from an emerging market and provides a replicable hybrid portfolio optimization framework for investors, portfolio managers, and financial institutions.
VL  - 1
IS  - 1
ER  -

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Author Information

Said Sami Al Hallaq

Department of Business Economics, Yarmouk University, Irbid, Jordan

Contact Email

http://orcid.org/0000-0002-5578-5824
Mohammad Ajlouni

Department of Finance, Yarmouk University, Irbid, Jordan
Anas Ghazalt

Department of Accounting, Arab Open University, Amman, Jordan

http://orcid.org/0000-0002-0718-1903
Mahmoud Al-Saadi

Department of Investment and Foreign Operations, Central Bank of Jordan, Amman, Jordan

http://orcid.org/0009-0006-3282-7100

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APA Style

Hallaq, S. S. A., Ajlouni, M., Ghazalt, A., Al-Saadi, M. (2026). A Hybrid Genetic Algorithm–analytic Hierarchy Process Approach to Optimal Portfolio Selection: Evidence from the Amman Stock Exchange. Innovation Business, 1(1), 37-54. https://doi.org/10.11648/j.ib.20260101.14

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ACS Style

Hallaq, S. S. A.; Ajlouni, M.; Ghazalt, A.; Al-Saadi, M. A Hybrid Genetic Algorithm–analytic Hierarchy Process Approach to Optimal Portfolio Selection: Evidence from the Amman Stock Exchange. Innov. Bus. 2026, 1(1), 37-54. doi: 10.11648/j.ib.20260101.14

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AMA Style

Hallaq SSA, Ajlouni M, Ghazalt A, Al-Saadi M. A Hybrid Genetic Algorithm–analytic Hierarchy Process Approach to Optimal Portfolio Selection: Evidence from the Amman Stock Exchange. Innov Bus. 2026;1(1):37-54. doi: 10.11648/j.ib.20260101.14

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@article{10.11648/j.ib.20260101.14,
  author = {Said Sami Al Hallaq and Mohammad Ajlouni and Anas Ghazalt and Mahmoud Al-Saadi},
  title = {A Hybrid Genetic Algorithm–analytic Hierarchy Process Approach to Optimal Portfolio Selection: Evidence from the Amman Stock Exchange},
  journal = {Innovation Business},
  volume = {1},
  number = {1},
  pages = {37-54},
  doi = {10.11648/j.ib.20260101.14},
  url = {https://doi.org/10.11648/j.ib.20260101.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ib.20260101.14},
  abstract = {This study investigates the effectiveness of a hybrid decision-support framework that integrates a Hybrid Genetic Algorithm (HGA) with the Analytic Hierarchy Process (AHP) to identify optimal investment portfolios in the Amman Stock Exchange (ASE). Using daily stock return data for companies listed on the ASE over the period January 1, 2015 to December 31, 2015, the HGA is employed to generate and evolve 10,000 candidate portfolios, each consisting of six stocks. The generated portfolios are evaluated based on the risk–return trade-off, and the efficient frontier is constructed. Subsequently, the AHP is applied to rank the efficient portfolios according to seven evaluation criteria: expected return, risk, beta, liquidity, Sharpe ratio, Treynor ratio, and Jensen’s alpha. The results demonstrate that the proposed HGA–AHP framework effectively identifies portfolios located on the efficient frontier and selects an optimal portfolio that exhibits superior risk-adjusted performance relative to the market benchmark. The hybrid approach successfully addresses nonlinear constraints and discrete asset selection issues that limit traditional optimization techniques. This study contributes empirical evidence from an emerging market and provides a replicable hybrid portfolio optimization framework for investors, portfolio managers, and financial institutions.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - A Hybrid Genetic Algorithm–analytic Hierarchy Process Approach to Optimal Portfolio Selection: Evidence from the Amman Stock Exchange
AU  - Said Sami Al Hallaq
AU  - Mohammad Ajlouni
AU  - Anas Ghazalt
AU  - Mahmoud Al-Saadi
Y1  - 2026/02/24
PY  - 2026
N1  - https://doi.org/10.11648/j.ib.20260101.14
DO  - 10.11648/j.ib.20260101.14
T2  - Innovation Business
JF  - Innovation Business
JO  - Innovation Business
SP  - 37
EP  - 54
PB  - Science Publishing Group
UR  - https://doi.org/10.11648/j.ib.20260101.14
AB  - This study investigates the effectiveness of a hybrid decision-support framework that integrates a Hybrid Genetic Algorithm (HGA) with the Analytic Hierarchy Process (AHP) to identify optimal investment portfolios in the Amman Stock Exchange (ASE). Using daily stock return data for companies listed on the ASE over the period January 1, 2015 to December 31, 2015, the HGA is employed to generate and evolve 10,000 candidate portfolios, each consisting of six stocks. The generated portfolios are evaluated based on the risk–return trade-off, and the efficient frontier is constructed. Subsequently, the AHP is applied to rank the efficient portfolios according to seven evaluation criteria: expected return, risk, beta, liquidity, Sharpe ratio, Treynor ratio, and Jensen’s alpha. The results demonstrate that the proposed HGA–AHP framework effectively identifies portfolios located on the efficient frontier and selects an optimal portfolio that exhibits superior risk-adjusted performance relative to the market benchmark. The hybrid approach successfully addresses nonlinear constraints and discrete asset selection issues that limit traditional optimization techniques. This study contributes empirical evidence from an emerging market and provides a replicable hybrid portfolio optimization framework for investors, portfolio managers, and financial institutions.
VL  - 1
IS  - 1
ER  -

Copy | Download