Abstract

Despite identical fundamentals, firms face divergent market reactions when targeted by bot-amplified misinformation. Why do some firms suffer multi-billion-dollar losses (e.g., Eli Lilly's $22 billion loss from falsified insulin pricing) while others remain protected? We integrate Signaling Theory and the Attention-Based View to explain heterogeneity through dual mechanisms and protective infrastructure. Bot-amplified misinformation operates through two pathways: corrupting information quality (Signaling Theory) and monopolizing investor attention (Attention-Based View). We theorize two protective mechanisms—firm reputation systems and platform governance interventions—that provide synergistic protection. We employ event study methodology analyzing publicly traded firms, integrating Twitter data, financial markets data, and platform governance records. Critical challenges include governance endogeneity and selection bias, which we address through difference-in-differences, instrumental variables, propensity score matching, firm fixed effects, and falsification tests. We seek feedback on instrument exogeneity and mechanism separability.

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