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

Abstract: This paper applies a Bayesian break method to studying the empirical time-varying relations between stock price ratios and subjective expectations across the market and 30 industry portfolios monthly from 1976 to 2020. Cash flow expectations unconditionally explain 80% of price variations since 2000 but their role is concentrated during recessions, especially among the hardest-hit industries such as Telecommunications during Tech Bubble, Financials during Great Recession, and Healthcare during Covid-19. Concurrently, discount rates explain the remaining 20% but their portion rises above 50% during the expansionary 2010s. Further tests show that cash flow expectations matter more under financial uncertainty. Inflation expectations, while accounting for 60% of price fluctuations before 2000, play a negligible role thereafter.

  1. Actions Speak Louder than Words (2022)

(with Kuntara Pukthuanthong and Guofu Zhou)

Abstract: We comprehensively examine the predictability of investor sentiment over daily returns across eight asset classes. We compare two types of sentiment measures, those based on news and social media and those based on trading information. We find that trade-based sentiment measures outperform their text-based counterparts in both in-sample and out-of-sample predictions across most of the assets but this outperformance is more evident over the long horizon forecasts. Additionally, while sentiment on Bitcoin, USD, gold, and oil positively predicts future returns, sentiment on stocks, T-Bill, and real estates is a negative return predictor. Finally, among the assets, only trade sentiment on Bitcoin can be used by real-time mean-variance investors to generate substantial economic gains. This highlights the difficulty of turning technical indicators or news media 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