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
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).
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.