The University of Chicago

Working Papers

The Impact of Prices on Analyst Cash Flow Expectations
Revised May 2023.
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.

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

Do Subjective Growth Expectations Matter for Asset Prices? (Job Market Paper)
Revised November 2022.
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. A 1% rise in annual investor growth expectations raises price only 7 to 16 basis points, an order of magnitude 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. To reconcile this small causal effect with the strong correlation of growth expectations and prices, I provide evidence of reverse causality. Using flow-induced trading to instrument for prices, I find that prices cause growth expectations.

Presentations:

Awards:

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

The Causal Impact of Macroeconomic Uncertainty on Expected Returns (In Revision, Reject and Resubmit at Review of Financial Studies).
Revised January 2022.
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.

Presentations:

Awards:

  • Liew Fama-Miller PhD Fellowship for Best 2nd Year Paper

High-Frequency Expectations from Asset Prices: A Machine Learning Approach
(with Sangmin Oh) Revised March 2022.
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.

Presentations:

Awards:

  • Arnold Zellner Doctoral Prize 2020

Publications

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.