Portfolio Optimization with Covariance from News-Derived Information Networks

Abstract

Intercompany relationships change over time, but they are difficult to incorporate into traditional return-based covariance estimates. This article proposes a practical three-step framework for transforming financial news into a covariance input for portfolio optimization. First, a large language model extracts dynamic company relationship networks from daily financial news. Second, a graph neural network refines these relationships by combining the news-derived network with market features, producing company representations that reflect both narrative information and observed market conditions. Third, the refined company similarities are converted into a covariance proxy and used in a global minimum variance portfolio. Compared with traditional return-based covariance estimators and text-embedding baselines, the proposed framework improves risk-adjusted performance, volatility control, and downside-risk management over the evaluation period. The results indicate that text-embedding baselines capture meaningful financial similarity but that document-level averaging can dilute discriminative firm-level signals, limiting their effectiveness as stand-alone covariance estimators. Our findings suggest that news-derived information networks can complement historical returns by providing a dynamic covariance input that reflects evolving economic relationships among firms.