What Hundreds of Economic News Events Say About Belief Overreaction in the Stock Market
We measure the nature and severity of a variety of belief distortions in market reactions to hundreds of economic news events using a new methodology that synthesizes estimation of a structural asset pricing model with algorithmic machine learning to quantify bias. We estimate that investors systematically overreact to perceptions about multiple fundamental shocks in a macro-dynamic system, generating asymmetric compositional effects when real-world events produce conflicting signals with counteracting market implications. We show that such events can lead the market to underreact to news, even when investors overreact to all shocks.
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