Taking the Road Less Traveled? Market Misreaction and Firm Innovation Directions

(with Zunda Winston Xu), June 2025

Semifinalist for FMA 2024 Best Paper Award (Asset Pricing & Investments)

Best Paper Award, USC Marshall PhD Conference in Finance

Presentations: SITE (2025), BEAM PhD (2025), SFS Cavalcade NA (2025), EFA (2025), MFA Regular Session (2025), AFA Regular Program (2025), FMA (2024), EGSC (2024), NFA PhD Session (2024), MFR Summer Session Poster (2024), ESSFM Evening Seminar (2024), USC Marshall PhD Conference (2024)

We propose that investors misreact differently to technological innovations based on novelty, and that these misreactions exert a real impact on firms' future innovations. First, using textual measures of novelty, we find that investors underreact to the issuance of path-breaking innovations but overreact to trend-following ones. Novel patent issuance predicts lower risk and positive forecast errors, consistent with a non-risk-based mispricing mechanism. A model where boundedly-rational investors are unsure about the true novelty of a patent at issuance, explains the empirical patterns well. Second, using exogenous distraction shocks, such as sensational news, we present causal evidence that, after disappointing returns to patent news, novel firms shift from creating and following up on novel innovations to copycatting. The findings highlight that investors' misreactions to patent novelty steer innovation away from higher-valued, groundbreaking research.

The Risk and Return of Impact Investing Funds

(with Jessica Jeffers and Kelly Posenau), August 2024

Journal of Financial Economics 161: 103928 (November 2024). Replication Package, Online Appendix.

We provide the first analysis of the risk exposure and risk-adjusted performance of impact investing funds, private market funds with dual financial and social goals. We introduce a dataset of impact fund cash flows and exploit distortions in VC performance measures to characterize risk profiles. Impact funds have a lower market β than comparable private market strategies. Accounting for β, impact funds underperform the public market, though not necessarily more so than comparable strategies. We consider alternative pricing models, accounting for sustainability and emerging markets risk. We show investors’ wealth portfolios and taste change the perceived financial merit of impact investing.

Causal Inference in Financial Event Studies

(with Paul Goldsmith-Pinkham), May 2025

Presentations: NBER SI (2025), Southern Economic Association (2024)

We study financial event studies—empirical settings where asset market returns assess the impact of information or policy changes. We show that abnormal return estimators are sensitive to factor model misspecification, making them inconsistent estimators of causal effects. We demonstrate that staggered event timing can mitigate this issue in shorthorizon studies but not in long-horizon analyses, where misspecification bias accumulates over time. Synthetic control methods emerge as a solution that avoids these issues by directly modeling counterfactual security paths without requiring correct specification of the factor structure. Our empirical applications to political connections (Acemoglu et al., 2016) and S&P 500 index inclusion (Greenwood & Sammon, 2025) demonstrate the practical implication of these methodological insights, particularly in settings that lack both short horizons and random event timing.

Context Dependence in Managerial Expectations

June 2024

Presentations: FMA (2025), SFA (2025)

I study the source and heterogeneity of biases in managerial expectations of their own firms and the market. Using micro data from the Duke CFO Survey, I find that firm managers overreact to news universally in a wide range of expectations of firms' revenue, expense, and financing decisions, as well as the market performance. I show that forecast errors are predictable by past realizations, suggesting an extrapolative structure in these beliefs. Second, I argue that managers' biases are context-dependent: biases are heterogeneous across firms in different conditions. I find that extrapolation is stronger for firms in worse financial conditions. Also, extrapolative errors in own-firm forecasts spill over to beliefs about other corporate outcomes and market returns. Third, managerial biases are asymmetric in positive versus negative news. Managers of good(bad)-credit-quality firms are more optimistic (pessimistic) to positive (negative) news. These findings are most consistent with ``affective updating'': firm-level shocks shift beliefs broadly about (potentially unrelated) outcomes. The context dependence of managerial beliefs provides novel channels to amplify distortions in firm real decisions.

Associative Memory is Machine Learning

(with Leland Bybee), September 2023

We document a relationship between memory-based models of beliefs and a general class of kernel methods from the statistics and machine learning literature. Motivated by this relationship, we propose a new form of memory-based beliefs which aligns more closely with the state of the art in the machine learning literature. We explore this approach empirically by introducing a measure of “narrative memory”– similarity between states of the world based on similarity in narrative representations of those states. Using textual embeddings extracted from conference call transcripts, we show that our estimates of memory-based beliefs explain variation in errors in long-term growth forecasts of IBES analysts. We conclude by discussing implications of this relationship for the literature on memory-based models of beliefs.

What do Investors Overreact to? An Empirical Analysis of Long-Term Earnings Growth Forecast

November 2022