Decomposing Firm-Level Crisis Responses from Incomplete Market Signals: Evidence from China's IT Sector During COVID-19

Abstract

Exogenous shocks generate heterogeneous behavioral responses across firms, yet event studies typically report only sector-level averages. This paper develops a multi-method approach combining causal identification (difference-in-differences with cluster-robust inference), unsupervised behavioral discovery (K-means trajectory clustering, Gaussian hidden Markov models), and cross-sectional resilience prediction (logistic regression with bootstrap inference) to decompose firm-level response heterogeneity from noisy market signals. We demonstrate the approach on 246 Chinese A-share IT firms (216 with complete data for all analyses) during the COVID-19 shock (January 2020), using 252 non-IT CSI 300 firms as controls. The return decline was market-wide, not IT-specific (DID p = 0.59); the IT-specific effect was elevated volatility (DID \b{eta} = 0.043, cluster-robust p < 0.001), with the effect surviving Benjamini-Hochberg correction across alternative specifications. Unsupervised clustering produced three categories of trajectories: fast recovery (36 companies, +29.7%), resilient/moderate (67 companies), and persistent drag (113 companies, -6.9%). Prior-to-crisis financial fundamentals did not predict resilience well (AUC = 0.64, 95% CI: 0.57-0.71), in line with efficient markets' incorporation of public information into stock prices. The combination of causal analysis, unsupervised learning, and prediction represents a reproducible framework which can be applied to crises in other market periods.