I am an economics PhD student at Harvard and a John M. Olin Fellow in Empirical Law and Finance at Harvard Law School. My research interests are in finance and behavioral economics. Prior to graduate school, I attended college at Harvard and graduated in 2018 with a BA in applied math.


Email: jtang01@g.harvard.edu

Twitter: @JohnnyJTang

My Harvard website: scholar.harvard.edu/johnnytang


Conflicting Interests and the Effect of Fiduciary Duty — Evidence from Variable Annuities

(with Mark Egan and Shan Ge)

Forthcoming, Review of Financial Studies

We show that sales of variable annuities are more responsive to the sales agents' financial interests than investors' and that a proposed US Department of Labor fiduciary rule drastically changed this market.


Extreme Events and Overreaction to News

(with Spencer Kwon)

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"


The Effects of a Global Minimum Tax on Corporate Balance Sheets and Real Activities: Evidence from the Insurance Industry

I show that the base erosion and anti-abuse tax (BEAT), a global minimum tax passed as part of the 2017 Tax Cuts and Jobs Act, significantly changed the internal capital allocation of multinational insurance companies, increased global risk-sharing, and increased product prices.

Presented at: NBER Economic Impacts of Interjurisdictional Tax Competition, University of Waterloo Tax Policy Symposium, Harvard Law School


Individual Heterogeneity and Cultural Attitudes in Credence Goods Provision

European Economic Review, July 2020

(Undergraduate honors thesis)

I show that seller over-treatment in a credence goods market is highly predictable using rich microdata.

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