The study of portfolio optimization has been dedicated to a significant amount of literature because of its critical practical importance in the sphere of portfolio investment. The various theoretical frameworks and methodological constructs expressed by scholars have been aimed at optimizing portfolios to a better degree, i.e., reducing risk at the expense of increasing returns. The current study introduces a systematic machine-learning model of portfolio optimization that involves the combination of recognized analytical techniques. The sensitivity of the drawdown ratio of 40 countries is examined in terms of a multi-SVM decision-making representation. The system of classification was very accurate with a rate of 97.5%. The suggested solution contains analytical modelling and controlled data-preprocessing stages that guarantee the consistency of feature recovery and normalization of massive international-oriented datasets. The system has a high level of flexibility to the dynamism of the market because it uses a wide range of financial indicators and volatility-based parameters. Further, the multi-SVM ensemble is based on nonlinear kernel mappings to reveal the complex relationships between the risk and return components that make the process offer more accurate portfolio rebalancing information. Relative investigations with the traditional frameworks affirm better stability, higher risk management, and better precision in forecasting. The empirical analysis is also put in perspective with respect to the recent machine learning and deep reinforcement learning-based frameworks of portfolio optimization published in the recent literature. Lastly, this framework provides a scalable basis of integration with reinforcement learning and hybrid AI-based decision systems, thus creating consistent and data-driven financial approaches to global portfolio management.
Biswas et al. (2026) studied this question.