Performance analysis of metaheuristic and risk-averse optimization techniques for portfolio management under volatile markets

Investment management uses portfolio optimization to strategically allocate financial assets in order to maximize return and minimize risk. In markets characterized by volatility, investors are increasingly keen on finding newer ways that strike a proper balance between risk and returns. This article gives a brief literature review on portfolio optimization techniques for return enhancement and risk minimization with a very selective sample of high-performing stocks from the S&P 500 index. In particular, a combination of technical and fundamental studies was used to pick 51 out of the 98 equities for the sample. To find the best investment strategies with different risk-return profiles, this study examines the application of advanced methodologies such as Bacterial Foraging Optimization (BFO), Fireworks Optimization (FWA), Cat Swarm Optimization (CSO), Bat Optimization, and a mean-Conditional Value at Risk (mean-CVaR). We used transaction costs and out-of-sample validations to ensure that these methods are realistic and that the results hold up under different conditions. The VIX index was used to measure market condition, while CVaR, cumulative returns, volatility, and the Sharpe ratio were used to measure portfolio performance. The mean-CVaR approach emphasizes Conditional Value at Risk with evaluation based on portfolio return, risk, and Sharpe ratio. Results suggest that CSO performed best in balancing between risk and return: it had the highest risk-adjusted return and at the same time a good balance. On the other hand, the mean-CVaR model is especially suitable for prudential investment strategies because it minimizes risk and loss. The article hence contributes to the existing literature by highlighting and comparing rarely used metaheuristic methods of optimization in portfolio optimization. Additionally, CSO and BFO rank among the best for their efficiency and simplicity, allowing asset managers and investors to use them. Results reveal that during market interruptions like the ones caused by COVID-19, the mean-CVaR model behaves consistently. This lends credence to the idea that it could be useful for risk-averse investor education and trial-support portfolios. The study demonstrates intervals in which different optimization strategies are effective, allowing investors to make more prudent and educated decisions.