Welcome to my webpage!
My name is Ódhrain McCarthy (pronounced: Oran McCarthy).
I am a Ph.D. candidate in Economics at New York University and I am on this year's ACADEMIC JOB MARKET!
Research Areas: Behavioral Finance, Asset Pricing, Innovation & Education Economics
Curriculum Vitae: Link
Abstract: In this study, I show investors' responses to news differ sharply depending on investor sentiment and are consistent with confirmation bias. Specifically, I consider a mechanism in which increases in investor sentiment mean investors are more likely to have optimistic priors meaning they overreact to good cashflow news that confirms their priors and underreact to bad cashflow news that disconfirms their priors. The reverse patterns hold when investor sentiment is low, with underreaction to good news and overreaction to bad news. The mechanism provides a unifying explanation for the co-existence of investor underreaction and overreaction. It also delivers novel predictions which are validated using analyst survey and return data. These results imply investors' processing biases are not constant (the dominant assumption thus far) and that asset price movements are best understood through investors' priors. This has far-reaching implications for the expectations formation literature.
Presented at NBER 2022 Behavioral Finance and WFA 2022 Macro-Finance Meetings.
Received '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: Trailing price ratios, such as the price-dividend and the price-earnings ratio, scale prices by trailing cash flow measures. They theoretically contain expected returns, yet, their performance in predicting stock market returns is poor. This is because of an omitted variable problem: trailing price ratios also contain expected cash flow growth. We show that structural changes in cash flow growth have undermined the power of trailing price ratios to predict returns. To construct an optimal valuation ratio, we propose scaling prices by cash flow forecasts (forward price ratios). We implement this idea for the S&P500 index using machine learning forecasts of firms’ cash flows. The out-of-sample explanatory power for one-year stock returns ranges from 7% to 11%, thereby beating all other predictors and helping to resolve the out-of-sample predictability debate (Goyal andWelch, 2008).
Abstract: We show that the accounting measure used to compute earnings has important asset pricing implications. Street earnings are superior to GAAP earnings at valuing stocks, because they ignore transitory items with little to no relevance for future firm performance. The price-to-earnings (PE) ratio constructed using Street earnings predicts returns, while the PE ratio constructed using GAAP earnings predicts entirely earnings growth (and does not predict returns). On the industry-level, only cross-sectional variation in Street PE ratios is related to meaningful variation in cross-sectional returns. The choice of the earnings measures also has a stark influence on which subjective expectations can explain PE ratio variation. This can resolve the debate on the importance of short-term versus long-term earnings expectations.
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.