Research

Publications

1. The Impact of Prices on Analyst Cash Flow Expectations: Reconciling Subjective Beliefs Data with Rational Discount Rate Variation

   Journal of Financial Economics, 2025

   This version: May 2025

   Note: This paper contains and extends results from my previous paper “Do Subjective Growth Expectations Matter for Asset Prices?”

   Presentations: Arizona State University, University of Virginia, MFA (2024), University of Wisconsin-Madison Junior Finance Conference

   Abstract: I show that prices impact analyst cash flow expectations and argue this impact can partially reconcile subjective beliefs data with asset pricing models in which investors have rational expectations and discount rate variation drives prices. Previous work argues that correlations of biased analyst cash flow expectations with prices and future returns contradict rational models and imply biased investor expectations distort prices. However, using two instrumental variables for price, I find increases in price unrelated to cash flow news raise analyst cash flow expectations. Based on this empirical finding, I propose a model with rational investors that matches key moments in beliefs data: analysts form biased cash flow expectations by learning from prices that contain discount rate variation. Thus, while stylized facts in beliefs data can be consistent with investors having biased expectations that distort prices, these facts can also be consistent with investors having rational expectations and analysts learning from prices.

2. Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference (with Pan Xu and Quanquan Gu)

   In Proc. of the 34th International Conference on Machine Learning (ICML) Sydney, Australia, 2017

Working Papers

1. Endogenous Elasticities: Price Multipliers Are Smaller for Larger Demand Shocks (with Jiacui Li)

   This version: October 2025

   Presentations: MFA (2026), Princeton, NBER Asset Pricing Fall 2025, NYU WAPFIN 2025, UNC Junior Finance Conference, NFA (2025), University of Hong Kong, Hong Kong Polytechnic University, Stanford Institute for Theoretical Economics (SITE) 2025, 2025 Mid-Atlantic Research Conference (MARC), University of Utah, The Ohio State University, University of Virginia

   Awards: 2025 Mid-Atlantic Research Conference (MARC) Outstanding Paper Award

   Abstract: How well can markets absorb large demand shocks? We address this question using price multipliers: the per-unit price adjustment investors require to absorb shocks. We find stock-level multipliers are smaller for larger contemporaneous and cumulative past shocks. Using holdings data, we find investors endogenously become more price elastic as larger shocks create larger price dislocations. These results are consistent with models where investors become more willing to absorb shocks when large dislocations create large profit opportunities, such as fixed adjustment costs or endogenous inattention. Our findings suggest large shocks expand rather than exhaust the capacity of markets to absorb shocks.

2. How Much Do Subjective Growth Expectations Matter for Asset Prices?

   This version: October 2025

   Note: This paper contains and extends results from my previous paper “Do Subjective Growth Expectations Matter for Asset Prices?”

   Presentations: University of Southern California Macro-Finance Conference, Rutgers Business School, AFA 2023 Annual Meeting, SFS Cavalcade North America 2022, 14^th Annual SoFiE Post-Conference, Transatlantic Doctoral Conference 2022, Machine Learning in Economics Summer Institute 2022, Chicago Joint Program and Friends Conference 2022, Chicago Booth Finance Brownbag

   Awards: SoFiE Prize for the Best Paper at the 2022 Early-Career Scholars Conference, Liew Fama-Miller PhD Fellowship for Best 3^rd Year Paper

   Abstract: I show that stock prices incorporate subjective growth expectations gradually rather than immediately, and that this gradual incorporation weakens the impact of biased expectations on prices. Previous work assumes immediate incorporation, so excessive variation in expectations creates excess volatility in prices. In contrast, I provide reduced-form evidence that expectation and price dynamics following cash flow news are consistent with gradual incorporation, not immediate incorporation. I then develop a structural learning model to quantify the degree of gradual incorporation. I find that only 10% to 30% of a given expectation change is immediately incorporated into price. Based on these findings, I propose and calibrate an asset pricing model in which gradual incorporation arises from slow-moving asset demand. The model shows that gradual incorporation weakens the impact of biased growth expectations on return volatility. Thus, biased growth expectations have a smaller impact on prices than previous work suggests.

3. The Causal Impact of Macroeconomic Uncertainty on Expected Returns

   Reject and Resubmit at Review of Financial Studies

   This version: January 2022

   Presentations: AFA 2022 Annual Meeting, 13^th Annual SoFiE Pre-Conference, Chicago Joint Program and Friends Conference 2021, Chicago Booth Finance Brownbag

   Awards: Liew Fama-Miller PhD Fellowship for Best 2^nd Year Paper

   Abstract: I quantify the causal impact of macroeconomic uncertainty on expected returns. The exogenous timing of macroeconomic announcements provides an instrument for uncertainty. Using realized returns and daily measures of macroeconomic uncertainty, I find announcements resolve uncertainty, which causes expected returns to fall. Under weak assumptions, macroeconomic uncertainty explains at most 32% of expected return variation. Under the additional, empirically justified assumption that other expected return drivers do not correlate with announcement timing, macroeconomic uncertainty explains 10% of expected return variation and a one standard deviation increase in macroeconomic uncertainty raises long-run expected returns by 173 basis points.

4. High-Frequency Expectations from Asset Prices: A Machine Learning Approach (with Sangmin Oh)

   This version: March 2022

   Presentations: 13^th Annual SoFiE Conference, 2021 SoFiE Machine Learning Virtual Conference, Bank of England Conference on Modeling with Big Data & Machine Learning: Measuring Economic Instability, 2020 Bergen FinTech Conference, Chicago Booth Finance Brownbag, Chicago Econ Macro / Monetary Reading Group.

   Awards: Arnold Zellner Doctoral Prize 2020

   Abstract: We propose a novel reinforcement learning approach to extract high-frequency aggregate growth expectations from asset prices. While much expectations-based research in macroeconomics and finance relies on low-frequency surveys, the multitude of events that pass between survey dates renders identification of causal effects on expectations difficult. Our method allows us to construct a daily time-series of the cross-sectional mean of a panel of GDP growth forecasts. The high-frequency nature of our series enables clean identification in event studies. In particular, we use our estimated daily growth expectations series to test the “Fed information effect.” Extensions of our framework can obtain daily expectations series of any macroeconomic variable for which a low-frequency panel of forecasts is available. In this way, our method provides a sharp empirical tool to advance understanding of how expectations are formed.

Work in Progress

1. The Origins of the Factor Zoo: Investors Weakly Substitute Across Stocks (with Carter Davis)