Spencer Yongwook Kwon

I am a graduate student in business economics at Harvard. 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.

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

Working Papers

Investor Composition and Overreaction

(with Michael Blank and Johnny Tang: Draft soon!)


How do we predict which asset-price booms go bust? We develop a model of financial markets with investor heterogeneity that yields a summary statistic for the degree to which an asset price overreacts to news: the gap in holdings of the asset by oversensitive investors versus rational investors. We use quarterly institutional holdings data to measure investors’ news sensitivity according to their tendency to purchase stocks after positive news, and compute from this measure the asset-level holdings gaps between oversensitive and rational investors. We find that investor news sensitivity is persistent over time, with the holdings gap measure able to forecast reversals or continuation of asset-price run-ups. Furthermore, the holdings gap measure serves as a powerful aggregator of different channels of overreaction, reflecting not only price extrapolation but also overreaction to various sources of non-price information, such as industry winners and fundamental growth.


Extreme Events and Overreaction to News

(with Johnny Tang)

Revise and Resubmit, Review of Economic Studies

We propose a systematic predictor of under-and-overreaction to news in financial markets: the extremeness of the associated distribution of fundamentals. We show that stock prices have more overreaction and greater trading volume to more extreme types of news. We show that this is consistent with diagnostic expectations, a model of belief formation based on the representativeness heuristic.

Previously titled "Reactions to News and Reasoning By Exemplars"


Overreaction in Expectations: Evidence and Theory

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

Revise and Resubmit, Quarterly Journal of Economics

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.


100 Years of Rising Corporate Concentration

(with Yueran Ma, Kaspar Zimmerman)

Revise and Resubmit, 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.


Published Papers

Memory and Probability

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

Quarterly Journal of Economics, Forthcoming

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), 2021.

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.