Quantifying the Impact of Macroeconomic and Geopolitical Events on Stock Market Behavior Using Time-Series Deviation Modeling

This paper constructs a statistical framework for measuring and comparing the market impact of macroeconomic and geopolitical events using a deviation-based scoring methodology. We collect daily adjusted closing prices for the S&P 500 and NASDAQ Composite indices over the period 2007–2024 and define a rolling 30-day mean as the baseline expected-return model. Abnormal returns are computed as deviations from this baseline and aggregated into Cumulative Abnormal Returns (CAR) over a ten-day post-event window. A composite Event Impact Score (EIS) is derived from three components: CAR magnitude, post-event volatility change, and peak single-day abnormal return, weighted at 0.5, 0.3, and 0.2 respectively, then normalized across all events for cross-event comparison. Applying this framework to twelve historical events spanning financial crises, health emergencies, geopolitical shocks, and monetary policy decisions, within the sample analyzed, economic policy responses to crises, specifically the Federal Reserve emergency cut and the CARES Act, produce the largest measured deviations, while geopolitical events exhibit the smallest and most short-lived market disruptions. Financial crises rank highest in volatility impact, while economic policy events are the most internally heterogeneous category. These results are consistent with the academic literature on event studies and contribute a portable, interpretable scoring system that can be extended to additional indices, sectors, and event types for future comparative market research.