Research

Working Papers

  1. The Impact of Prices on Analyst Cash Flow Expectations

    This version: November 2023

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

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

    Abstract: I demonstrate that prices impact equity research analyst cash flow expectations. Using several instruments based on mutual fund flow-induced trading, I find analysts update their long- and short-term cash flow expectations in response to price changes caused by noise trading unrelated to cash flow news. These results have important implications for interpretations of analyst expectations. These results are inconsistent with models featuring homogeneous investor and analyst cash flow expectations that do not depend on prices. However, they are consistent with models in which cash flow expectations endogenously depend on prices, such as models with dispersed information or price extrapolation.

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

    This version: October 2023

    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, 14th 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 3rd Year Paper

    Abstract: I find that the causal effect of subjective growth expectations on asset prices is far smaller than standard models suggest. To quantify this causal effect, I construct an asset demand model in which Bayesian investors learn from analysts and other signals. Leveraging holdings, prices, and beliefs data, I find a 1% rise in annual investor growth expectations raises price by 60% to 90% less than in standard models. This small causal effect arises from the limited passthrough of beliefs to asset demand, and is consistent with small price elasticities of demand.

  3. The Causal Impact of Macroeconomic Uncertainty on Expected Returns

    In Revision, Reject and Resubmit at Review of Financial Studies

    This version: January 2022

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

    Awards: Liew Fama-Miller PhD Fellowship for Best 2nd 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: 13th 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.

Publications

  1. 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