Intelligent Portfolio Construction with Artificial Intelligence and Strategic Decision Models for Superior Asset allocation and Risk Control

Abstract

In this article, the authors suggest an extremely smart conceptual model for intelligent portfolio construction using Artificial Intelligence (AI) and Machine learning technologies to transcend the inherent structure limitations of the traditional Modern Portfolio Theory (MPT) and the mean-variance optimization. None of the conventional models take into account non-stationary returns distribution, structural discontinuity, fat tailed risks in the present day financial markets. The proposed architecture enhances the predictive engines of returns and covariance estimation through multi-modal data ingestion, unsupervised dimensionality reduction, and cutting-edge machine learning predictive engines such as gradient boosting and deep networks. In addition, the framework involves dynamic Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) limits, transaction cost-sensitive execution, explainable AI (XAI) to regulatory transparency and stringent operational controls required to deliver high-quality risk-adjusted returns and capital conservation.