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

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.