Causal Networks and Risk Contagion of Technology Stocks under Macroeconomic Policies: An Informatics-Driven Study

This paper investigates how macroeconomic policies shape the dynamics of leading technology stocks-Google (GOOGL), Apple (AAPL), and Microsoft (MSFT)through the lens of advanced informatics and service science.Using daily stock price data (2020-2025) alongside macroeconomic indicators (interest rates, inflation, and GDP growth), the study applies econometric techniques including Granger causality tests, Kalman filtering, causal network analysis, and bootstrap validation.Results reveal that inflation exerts the strongest negative pressure on Apple, while GDP growth most benefits Microsoft.Correlation and contagion indices further show that systemic risks intensify during market turbulence, reducing the diversification potential of technology portfolios.By integrating macroeconomic indicators with dynamic contagion modeling, the study provides an informatics-driven framework for monitoring systemic risk in technology markets.The findings offer both theoretical contributions to the literature on financial informatics and practical implications for risk management, decision support, and policy design in the digital economy.