Cardinality Constrained Portfolio Selection Strategy Based on Hybrid Metaheuristic Optimization Algorithm

Investment is one of the necessary economic activities, and stock market investment attracts investors due to its profitability, transparency, and liquidity. But the market offers a variety of securities with diverse returns and risk factors. The experts in the field always tried to analyze the stock market securities to select the best combination of securities. Over time, several innovative techniques have been developed to optimize investment activity by minimizing the risk and increasing the return. This paper proposes a hybrid portfolio selection strategy using ant lion optimization (ALO) and cuckoo search (CS) to minimize portfolio risk and maximize mean return. An experimental study evaluates the proposed strategy by comparing it with state-of-the-art methods on the standard benchmark dataset of the German stock exchange (100 stocks). The study exhibits the proposed strategy’s better performance among GA, SA, and TS-based solution approaches.