Ai-Driven Portfolio Analytics for Tokenized Real-World Assets: Assessing Diversification, Risk and Optimal Asset Allocation

Abstract

Tokenized real world assets are becoming an important segment of global financial markets. The paper explores the potential of AI-powered portfolio analytics in diversifying and managing risks, as well as in optimal asset allocation, for portfolios incorporating tokenized real world assets. The study constructs a framework of traditional portfolio theory and machine learning methods for return prediction and risk estimation. The framework is based on the well-known mean variance optimization methods proposed by Markowitz and extended by gradient boosting models like XGBoost and reinforcement learning models for dynamic allocation. The paper also provides a survey of explainable artificial intelligence techniques that enable investors to understand the model-based allocation decisions. A set of illustrative results tables and figures are provided to illustrate the calculation and interpretation of diversification metrics and risk metrics and allocation weights in this framework. The findings suggest that portfolios that combine tokenized real estate with tokenized commodities with traditional equities and bonds, may yield a more advantageous risk return profile than conventional portfolios. The analysis also reveals that the correlation of tokenized assets is different from those of traditional assets, which opens up new diversification opportunities. The paper explores implications for asset managers and regulators, and stresses the need for transparency and accountability in algorithmic asset allocation systems. The study finds that AI-based analytics could meaningfully enhance the process of portfolio construction for RWAs, as long as the model outputs are interpretable and consistent over time and market conditions.