Johnathan Loudis
Johnathan Loudis
Assistant Professor of Finance
Faculty Page
Johnathan Loudis is an Assistant Professor of Finance at the University of Notre Dame. He received his PhD in financial economics from the University of Chicago through a joint degree program between the Booth School of Business and the Kenneth C. Griffin Department of Economics. His research interests lie broadly in empirical asset pricing, asset pricing theory, and macro-finance. Johnathan also holds masters degrees in economics and materials science. He has previously worked at GE Global Research, SustainX (an alternative energy startup), and Oliver Wyman Financial Services.
Highlighted Research:
“Expectations in the Cross Section: Stock Price Reactions to the Information and Bias in Analyst Expected Returns”. Working Paper 2019.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3342507
Abstract:
This paper provides evidence that the market does not efficiently incorporate expected returns implied by analyst price targets into prices. I use a novel decomposition to extract information and bias components from these analyst-expected returns and develop an asset pricing framework that helps interpret price reactions to each component. A one-standard-deviation increase in the information (bias) component is associated with a five (one) percentage point increase in announcement-month returns. The positive reaction to bias implies the market does not fully debias analyst-expected returns before incorporating them into prices. Prices overreact to bias and reverse their initial reaction within three to six months. Prices underreact to information and returns drift an additional one percentage point beyond their initial reaction in the following 12 months. Announcement-window returns forecast future returns, which provides model-free evidence of underreaction, and that underreaction dominates overreaction. Trading against underreaction generates average monthly returns of 1.12% with a Sharpe ratio of 1.08, and the returns survive controlling for exposure to many standard factors.