Welcome to my webpage!

My name is Ódhrain McCarthy and I am a Ph.D. candidate in Economics at New York University. 

Research Areas: Behavioral Finance, Asset Pricing, Innovation & Education Economics 

Curriculum Vitae: Link


Heterogeneous Beliefs and Stock Market Fluctuations.  with Hillenbrand, Sebastian

WFA Brattle Group Ph.D. Award for Outstanding Research. 

Abstract: Stock prices aggregate the beliefs of different investors. Using this insight, we estimate the fraction of stock market investors holding survey beliefs. We find that 42% of investors hold beliefs matching those of equity analysts and 25% hold beliefs as observed in individual investor return surveys. Together with risk aversion proxies and rational cash flow forecasts constructed using machine learning techniques, survey beliefs explain 87% of stock market fluctuations. Because investors likely form their beliefs by extrapolating prices and cash flows, we find stock prices would fluctuate 50% less if all investors held rational beliefs. Allowing for investor heterogeneity and using a price-driven price-to-earnings ratio reconciles prior studies.

Abstract:  Stock valuation ratios contain expectations of returns, yet, their performance in predicting returns has been rather dismal. This is because of an omitted variable problem: valuation ratios also contain expectations of cash flow growth. Time-variation in cash flow volatility and a structural shift towards repurchases have magnified this omitted variable problem. We show theoretically and empirically that scaling prices by a forward measure of cash flows can overcome this problem and yields optimal return-predictors. We then construct a new measure of the forward price-earnings ratio for the S&P index based on earnings forecasts using machine learning techniques. The out-of-sample explanatory power for predicting one-year aggregate returns with our forward price-to-earnings ratio ranges from 7% to 11%, thereby beating all other predictors (Welch and Goyal, 2008).

The Effect of the Prior Teacher on Value-Added.  with Gilraine, Michael

Conditionally Accepted at `Review of Economics and Statistics'.  

Abstract: We show that teachers’ value-added (‘VA’) depend on the quality of the teachers that preceded them. To do so, we use administrative data from North Carolina and find that a one-standard deviation increase in last grade’s mean teacher VA causes a 0.08σ decrease in teacher VA. Controlling for prior teacher assignment eliminates this bias. Under a benchmark policy that releases teachers in the bottom five percent of the VA distribution, thirty-two percent of teachers are incorrectly released using traditional techniques. Our results highlight the importance of incorporating dynamic features of education production into the estimation of teacher quality.


Technology Space versus Product Market Space: Evidence from M&As.  with Camelo, Felipe