Working Papers
Mental Models and Financial Forecasts
(with Paul Décaire and Marius Guenzel). April 2025.
Abstract. We uncover financial professionals’ mental models—the reasoning they use to explain their quantitative forecasts. We organize our analysis around a framework of top-down attention, where analysts endogenously choose both a valuation method and how to allocate attention across variables. Using the near-universe of 1.6 million equity analyst reports, we collect the valuation methods analysts adopt to compute their price targets. We then prompt large language models (LLMs) on a subset of 110,000 reports to extract 4.8 million lines of reasoning—each combining a topic, valuation channel, time horizon, and sentiment. To validate the reliability of our output, we introduce a multi-step LLM prompting strategy and new diagnostic tools. We document four main findings. (1) Analysts exhibit sparse mental representations, focusing on a limited set of topics, that are primarily related to top-line items, and forward-looking. (2) The choice of valuation methods and topic focus is closely linked. (3) There is substantial disagreement among analysts, and differences in attention weights to firm-specific variables are a bigger source of disagreement than differences in valuation weights on those variables. (4) Lastly, variation in mental models aligns with key asset pricing patterns both in the time-series and in the cross-section.
Underreaction and Overreaction in Inference and Forecasting
(with Alex Imas). February 2025.
Abstract. We study variation in belief formation both within and across different decision domains as a function of context and the relevant features. We begin by mapping the commonly-studied inference and forecasting domains to the same underlying framework. In the model, the decision context cues a mental representation of the information environment, and limited attention leads to more sensitivity to salient features compared to neglected ones. A series of experiments show that variation in over and underreaction across domains is due to inference and forecasting cueing different mental representations of the same information environment, leading to a reliance on different cognitive defaults. Variation in over and underreaction within domains is instead explained by the allocation of attention across features, which generates differences in sensitivity away from the cognitive defaults. When varying a single feature, we recover the standard result of overreaction to weaker signals and underreaction to stronger signals. However, once we allow for attention to interact with multiple features, we show that this comparative static is modulated by attention, and can even be fully reversed. Empirical tests further identify the mechanism by directly manipulating attention across features and by introducing exogenous variation in cued representations.
Partial Equilibrium Thinking, Extrapolation, and Bubbles
(with Paul Fontanier). Online Appendix. August 2024.
Review of Financial Studies, Revise and Resubmit.
Abstract. We develop a dynamic theory of “Partial Equilibrium Thinking” (PET), which micro-founds time-varying price extrapolation: extrapolative beliefs are present at all times, but only sometimes manifest themselves in explosive ways. To study this systematically, we formalize the distinction between normal times shocks and “displacement shocks” (Kindleberger 1978). In normal times, PET generates constant extrapolation, contrarian trading, and price momentum. Instead, following a displacement shock that increases uncertainty, PET leads to stronger and time-varying extrapolation and momentum trading, triggering bubbles and endogenous crashes. Our theory sheds light on both normal times market dynamics and Kindleberger’s narrative of bubbles within a unified framework.
Publications
Expectations and Learning from Prices
(with Paul Fontanier). Online Appendix. January 2024.
Review of Economic Studies, 2024.
Abstract. We study mislearning from equilibrium prices, and contrast this with mislearning from exogenous fundamentals. We micro-found mislearning from prices with a psychologically founded theory of “Partial Equilibrium Thinking” (PET), where traders learn fundamental information from prices, but fail to realize others do so too. PET leads to over-reaction, and upward sloping demand curves, thus contributing to more inelastic markets. The degree of individual-level over-reaction, and the extent of inelasticity varies with the composition of traders, and with the informativeness of new information. More generally, unlike mislearning from fundamentals, mislearning from prices i) generates a two-way feedback between prices and beliefs that can provide an arbitrarily large amount of amplification, and ii) can rationalize both over-reaction and more inelastic markets. The two classes of biases are not mutually exclusive. Instead, they interact in very natural ways, and mislearning from prices can vastly amplify mislearning from fundamentals.
Work in Progress
Expectations and Inelastic Markets