1. What Drives Stock Prices along Business Cycles? (2022)

Abstract: This paper provides novel evidence of the time-varying roles of subjective expectations in explaining stock price variations across the market and 30 industry portfolios monthly from 1976 to 2020. Cash flow expectations matter more under financial uncertainty and recessions, especially among the hardest-hit industries such as Telecommunications during the Dot-com Bubble, Financials during the Great Recession, and Healthcare during the Covid-19 pandemic. Conversely, discount rates explain more price variations during the expansionary 2010s. Finally, inflation expectations, while accounting for 60% of price fluctuations in the high inflationary environment before 2000, play a negligible role thereafter.

  1. Investor Sentiment and Asset Returns: Actions Speak Louder Than Words (2022)

(with Kuntara Pukthuanthong and Guofu Zhou)

Abstract: We analyze the daily predictability of investor sentiment across four major asset classes and compare sentiment measures based on news and social media with those based on trade information. For the majority of assets, trade-based sentiment measures outperform their text-based equivalents for both in-sample and out-of-sample predictions. This outperformance is particularly noticeable in long-term forecasts. However, real-time mean-variance investors can only achieve economic gains using Bitcoin trade sentiment, suggesting the challenge of transforming sentiment into daily profitable trading strategies.

  1. War Risk: Time Series and Cross-sectional Evidence from the Stock and Bond Markets (2021)

(with Kuntara Pukthuanthong)

Abstract: We employ a semi-supervised topic model to extract the rare disaster risks and economic narratives from 7,000,000 NYT articles over 160 years. Our approach addresses the look-ahead bias and changes in semantics. War positively predicts market return in- and out-of-sample, while the economic narratives only predict in-sample. The predictability of War increases over time and is robust when extracted from WSJ. War as a solo factor prices characteristics-sorted portfolios with a negative risk premium and outperform some multifactor benchmarks when pricing machine learning-based nonlinear portfolios with an R-squared of 54%. Our study lends support to the time-varying disaster risk model.

  1. Change in Consumption Growth and the Cross-Section of Expected Returns (2020)

(with Kuntara Pukthuanthong)

Abstract: We conduct empirical tests of a simplified version of the ratio habit model developed in Abel (1990), in which habit is extended beyond the preceding period. We show that change in four-year consumption growth—the measure of consumption resulting from our ratio habit preference—explains the joint equity premium–risk-free rate puzzle with a risk aversion coefficient much lower than any existing consumption measures under the standard consumption model. This outperformance of our ratio habit model over the standard model is robust across 18 non-U.S. countries. From 1928-2017, change in four-year consumption growth encompasses other consumption measures in explaining the cross-sectional variation of expected returns on various portfolios and it is the only consumption measure that passes the robust tests of the factor risk premium proposed by Kleibergen and Zhan (2020). While our measure constructed from nondurables does better at pricing the equity premium and risk-free rate, our service-based measure outperforms in explaining the cross-sectional variation of stock returns.

  • Presentations: University of Missouri-Columbia 2020