Spencer Yongwook Kwon

I am an assistant professor of economics at Brown University. My main research interest is to understand how people respond to information, and to explore financial and macroeconomic implications. My work uses a variety of methods, including lab experiments, theoretical modeling, and empirical asset pricing.

Contact: spencer_kwon@brown.edu

You can find my CV here and learn about my research below.

Working Papers

How People Use Statistics

(with Pedro Bordalo, John Conlon, Nicola Gennaioli, and Andrei Shleifer)
Revise and Resubmit, Review of Economic Studies

We document two new facts about the distributions of answers in famous statistical problems: they are i) multi-modal and ii) unstable with respect to irrelevant changes in the problem. We offer a model in which, when solving a problem, people represent each hypothesis by attending “bottom up” to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used, others are neglected. The model unifies biases in judgments about i.i.d. draws, such as the Gambler’s Fallacy and insensitivity to sample size, with biases in inference such as under- and overreaction and insensitivity to the weight of evidence. The model makes predictions about how changes in the salience of specific features should jointly shape the prevalence of these biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Bottom-up attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of the Bayes rule.

Investor Composition and Overreaction

(with Michael Blank and Johnny Tang)

Do stock price run-ups predictably revert? We develop a model of financial markets with two types of investors: rational investors and "oversensitive" investors who react excessively to salient public news. The model yields a summary statistic for the degree to which a stock price has overreacted to news: the gap in holdings between oversensitive and rational investors. We compute this measure empirically using quarterly institutional holdings data. We first measure each investor's news sensitivity using their tendency to purchase stocks that have experienced positive earnings announcements. Consistent with our model's premise, we find that news sensitivity is a persistent investor characteristic. We next aggregate our investor-level measure to the stock level to compute the asset-level holdings gap between oversensitive and rational investors. A larger holdings gap forecasts less continuation in stock prices and greater reversals in the long-run, especially for extreme price run-ups. Furthermore, our holdings gap aggregates several distinct channels of overreaction, including both price extrapolation and overreaction to non-price information. 

Extreme Events and Overreaction to News 

(with Johnny Tang)

Revise and Resubmit, Review of Economic Studies

The presence of both systematic under-and-overreaction to news in financial markets is a major puzzle. We propose a systematic predictor of under-and-overreaction to news: the extremeness of the associated distribution of fundamentals. Using a comprehensive database of corporate news events, we identify substantial heterogeneity in both reactions to news and extremeness of fundamentals across types of corporate events. We document overreaction to more extreme event-types, such as leadership changes, M&A, and customer announcements, and underreaction to less extreme event-types such as earnings announcements. We show this is consistent with diagnostic expectations, a model of belief formation based on the representativeness heuristic. The model further predicts greater trading volume holding fixed fundamentals and more sensitive belief changes to more extreme corporate events, which we confirm in the data. We calibrate our model and show that it quantitatively matches the key features in our data. 

Previously titled "Reactions to News and Reasoning By Exemplars"

Published Papers

100 Years of Rising Corporate Concentration 

(with Yueran Ma, Kaspar Zimmerman)

Forthcoming, American Economic Review

We collect data on the size distribution of U.S. corporate businesses for nearly 100 years, and find that corporate concentration in the U.S. economy has been increasing persistently over the past century. We find that the timing and the degree of rising concentration in an industry align closely with the investment intensity in research and development and information technology, as well as higher output growth.  The evidence suggests that the long-run trends of rising corporate concentration reflect increasingly stronger economies of scale.

Overreaction in Expectations: Evidence and Theory 

(with Hassan Afrouzi, Augustin Landier, Yueran Ma, and David Thesmar)

Quarterly Journal of Economics, 2023

We investigate biases in expectations across different settings through a large-scale randomized experiment where participants forecast stable stochastic processes. We find that forecasts display significant overreaction to the most recent observation. Moreover, overreaction is especially pronounced for less persistent processes and longer forecast horizons. To explain the observed patterns of overreaction, we develop a tractable model of expectations formation with costly information processing. Our model closely fits the empirical findings and generates additional predictions that we confirm in the data.

Memory and Probability 

(with Pedro Bordalo, John Conlon, Nicola Gennaioli, and Andrei Shleifer)

Quarterly Journal of Economics, 2022.

We present a model where people estimate probabilities by retrieving experiences from memory. The model accounts for and reconciles a variety of conflicting empirical findings, such as overestimation of unlikely events when these are cued vs. neglect of non-cued ones, the availability heuristic, the representativeness heuristic, conjunction and disjunction fallacies, as well as over vs. underreaction to information in different situations. The model makes new predictions on how the content of a hypothesis (not just its objective probability) affects probability assessments by shaping ease of recall. We experimentally evaluate these predictions and find strong experimental support.   

Diagnostic Bubbles 

(with Pedro Bordalo, Nicola Gennaioli, and Andrei Shleifer) 

Journal of Financial Economics, 2021.

We study the interaction of diagnostic expectations with two well-known mechanisms: learning from prices and speculation (buying for resale). With diagnostic (but not with rational) expectations, these mechanisms lead to price paths exhibiting three phases: initial underreaction, followed by overshooting (the bubble), and finally a crash. Speculation amplifies the bubble, with optimistic investors buying to sell to even more optimistic investors in the future.