We model a financial market where some agents mistakenly attribute any price change they observe to new information alone, when in reality part of the price change is due to other agents’ buying/selling pressure, a form of bounded rationality that we refer to as “Partial Equilibrium Thinking” (PET). PET provides a micro-foundation for price extrapolation, where the degree of extrapolation depends on the informational edge of informed agents. In normal times, this edge is constant and bubbles and crashes do not arise. By contrast, following a large one-off innovation in fundamentals that temporarily wipes out informed agents' edge (a “displacement event”), extrapolation by PET traders is initially very aggressive but then gradually dies down, leading to bubbles and endogenous crashes. Micro-founding the degree of extrapolation in this way allows us to shed light on both normal market dynamics and on the Kindleberger (1978) narrative of bubbles within a unified framework.
We develop a theory of “Partial Equilibrium Thinking” (PET), a type of misinference whereby agents fail to understand the general equilibrium consequences of their actions when inferring information from endogenous outcomes. PET generates a two-way feedback between outcomes and beliefs, which can lead to arbitrarily large deviations from fundamentals. In financial markets, PET equilibrium outcomes exhibit over-reaction, excess volatility, high trading volume, and return predictability. We draw a distinction between models of misinference and models with biases in Bayesian updating, and study how these two departures from rationality interact. We show that misinference from mistakenly assuming the world is rational can vastly amplify biases in Bayesian updating, and that the distinction between these two biases can have important quantitative implications.
We propose a behavioral theory of credit cycles that rests on model misspecification. Banks infer information about the underlying quality of the pool of borrowers by looking at credit volume, but use a misspecified model to do so. Their inferred beliefs then influence their current lending standards, which in turn lead to changes in aggregate credit volume and future beliefs, thus giving rise to a two-way feedback between outcomes and beliefs. We highlight three sets of results. First, following a positive shock, agents' beliefs become decoupled from fundamentals, and banks perceive the quality of the pool of borrowers to be increasing even when it is in fact decreasing. This helps rationalize the well-established fact that booms are associated with decreasing credit spreads and a deteriorating quality of funded borrowers. Second, we allow the quality of the pool of borrowers to be endogenous, and we show how the interaction of our behavioral bias with dynamic strategic substitutabilities in lending standards generates endogenous credit cycles with systematic reversals. Third, we turn to forecast errors to show that since the influence of beliefs on aggregate credit volume is state-dependent, the size of the behavioral bias is also state-dependent, and the response to positive and negative shocks is asymmetric.
Regressions aimed at detecting forecast errors predictability are a widespread tool to assess deviations from the full information rational expectations equilibrium benchmark. We show that interpreting these regression coefficients as evidence of over- or under-reaction may be misleading when the object of interest is an endogenous variable. We simulate scenarios where an econometrician would detect short-term under-reaction and long-term over-reaction, when in reality the equilibrium outcome exhibits over-reaction at all horizons when compared to the rational expectations benchmark. We then turn to stock market expectations data and compare regression results on earnings and dividends (exogenous variables) with those on price targets (endogenous variable). Regressions of forecast errors predictability are still instructive of the precise biases agents have, but only when they are interpreted through the lens of the underlying structural model.