Enhancing Stock Portfolio Optimization Based on a Hybrid Approach Using Artificial Bee Colony Optimization and Firefly Optimization

The goal of stock portfolio optimization, a crucial activity in finance, is to strike the ideal balance between risk and return. In this study, we suggest a hybrid strategy that combines Firefly Optimization (FO) and Artificial Bee Colony Optimization (ABC) to improve the performance of stock portfolio optimization. Enhancing portfolio returns while reducing volatility is the main goal. Historical stock return data is used to put the hybrid strategy into practice. The objective function used to formulate the portfolio optimization issue takes the expected returns and risk of the portfolio into account. A penalty term is also included to enforce the requirement that the portfolio weights amount to one. The hybrid algorithm moves through two phases. First, the solution is investigated using the Artificial Bee Colony Optimization technique. The Firefly Optimization technique is then used to further refine the solutions. The combination of these two optimization strategies enables the algorithm to explore numerous possibilities and converge to high-quality portfolio allocations in a better timely manner. We see large gains in the Sharpe and Sortino ratios, indicating risk-adjusted returns. The hybrid strategy consistently achieves higher annualized returns over multiple time intervals, namely 5, 10, and 15 trading days over traditional methods. The hybrid approach, provides a robust and effective tool for optimizing stock portfolios. This strategy can be used to a variety of financial decision-making scenarios and has the potential to give investors and portfolio managers with higher risk-adjusted returns.