Stock Market Duration
At the peak of the tech bubble, only 0.57% of market valuation comes from dividends in the next year. Taking the ratio of total market value to the value of one-year dividends, we obtain a duration of 175 years. In contrast, at the height of the global financial crisis, more than 2.2% of market value is from dividends in the next year, implying a duration of 46 years. What drives market duration? We find that market participants have limited information about cash flow beyond one year. Therefore, an increase in market duration is due to a decrease in the discount rate rather than good news about long-term growth. Accordingly, market duration negatively predicts annual market return with out-of-sample $R^2$ of 15%, outperforming other predictors in the literature. While the price-dividend ratio reflects the overall valuation level, market duration captures the slope of the valuation term structure. We show that market duration, as a discount rate proxy, is a critical state variable that augments the price-dividend ratio in spanning the (latent) state space for stock-market dynamics.
The Impact of Beliefs on Credit Markets: Evidence from Rating Agencies
We analyze the impact of rating agencies’ beliefs on credit markets. We measure their beliefs as the difference between their forecasts of aggregate credit spreads and the consensus. When rating agencies become more optimistic, they issue higher ratings even though their forecasts do not predict future credit spreads. This optimism leads to lower initial bond yields and subsequent negative excess returns. Firms respond by increasing their leverage and investment. Finally, rating agencies become more optimistic as their head economists’ property values increase. Our analysis shows how subjective beliefs drive aggregate financing and investment through mispricing in credit markets
Notre Dame, McGill, Peking University, SUFE, CICF 2022, AsianFA 2022, Office of the Comptroller of the Currency, Chinese University of Hong Kong, Shenzhen, University of Georgia, Boulder Summer Conference on Consumer Financial Decision Making, NFA 203, AFA 2024
Cross-Sectional Asset Prices under the Impact of Noise Trading Flows: A Factor Framework
Financial Markets and Corporate Governance Conference Runner-up for Best Paper
We propose that noise trading flows impact cross-sectional asset prices through systematic risk factors. In our model, asset-level flows, when aggregated at the factor level, drive fluctuations in factor prices and risk premiums. These factor- level price impacts in turn drive cross-sectional asset prices according to the asset’s risk exposure. Empirically, our model explains the price impacts and cross impacts of underlying assets with a few risk factors. Moreover, the model-implied trading strategy, designed to optimally exploit the reversion in price impacts, delivers strong and robust investment outcomes
Notre Dame, Johns Hopkins Carey, RUC-VUW Joint Virtual Research Workshop, 6th Annual Wolfe Global Quantitative and Macro Investment Conference, Federal Reserve Board, Campbell & Company, MFA Annual Meeting 2023, Southern Methodist University, FMCG 2023, SoFiE 2023 Conference, CICF 2023, Chinese University of Hong Kong, City University of Hong Kong, 10th SAFE Asset Pricing Workshop, UT Dallas 2023 Fall Finance Conference
Factor Demand and Factor Returns
CFAM-ARX Paper Award, Finance Down Under Conference, 2022
Chicago Quantitative Alliance Academic Competition Second Prize, 2022
We propose a novel source of predictable price pressure resulting from mutual funds’ factor rebalancing behavior. When a fund’s factor demand is persistent, it needs to rebalance the portfolio’s factor exposure, leading to predictable trading at the stock level. This form of predictable trading operates independently from trading induced by retail flows and has distinct implications for cross-sectional return predictability. Consistent with demand-induced price pressure, stocks whose characteristics are well-matched with the underlying funds’ factor demand experience more buying pressure and higher returns, whereas mismatched stocks experience more selling and lower returns. We calculate the scale of factor rebalancing and estimate an average factor demand elasticity of -0.23.
RCFS/RAPS Conference at Baha Mar 2019, CICF 2019, SGF 2019, AFA 2021, MFA 2021, NFA 2021, Finance Down Under 2022, SFS Cavalcade North America 2022, FIRS 2022, 10th Helsinki Finance Summit, EFA 2022, Chicago Quantitative Alliance Academic Competition 2022; Birkbeck (University of London), BlackRock, Cambridge, LSE, Notre Dame, Peking University, USI Lugano, Yale SOM
Under- and Overreaction in Yield Curve Expectations
I document a robust pattern in how Treasury market participants’ yield curve expectations respond to new information: forecasts for short-term rates underreact to news while forecasts for long-term rates overreact. I propose a new explanation of this based on ``autocorrelation averaging,’’ whereby, due to limited processing capacity, forecasters’ estimate of the autocorrelation of a given process is biased toward the average autocorrelation of all related processes. Consistent with this view, forecasters overestimate the autocorrelation of the less persistent term-premium component of interest rates and underestimate the autocorrelation of the more persistent short-rate component; a calibrated model quantitatively matches the documented pattern of misreaction. Moreover, banks’ allocations to Treasuries vary positively with their expectations of bond returns and misreaction proxies can strongly predict future short- and long-term bond returns, respectively.
Hong Kong University of Science and Technology, Cheung Kong Graduate School of Business, University of Hong Kong, National University of Singapore, Chinese University of Hong Kong, Notre Dame, Michigan Ross, University of Florida, Cornerstone Research, Yale SOM, 2021 WFA
Rediscover Predictability: A Duration-Based Approach
16th Paris December Finance Meeting Best Paper Award
The ratio of long- to short-term dividend prices, “price ratio” ($pr_t$), predicts annual market return with an out-of-sample $R^2$ of 19%, subsuming the predictive power of price-dividend ratio ($pd_t$). After controlling for $pr_t$, $pd_t$ predicts dividend growth with an out-of-sample $R^2$ of 30%. Our results hold outside the U.S. An exponential-affine model shows that the key to our findings is the (lack of) persistence of expected dividend growth. We find the expected return is countercyclical and responds strongly to monetary policy shocks. As implied by ICAPM, shocks to $pr_t$, the expected-return proxy, are priced in the cross-section.
LBS Trans-Atlantic Doctoral Conference, Yale SOM, Econometric Society Annual Meeting, 2018 NFA, Fall Finance Conference at UT Dallas, Örebro Workshop on Predicting Asset Returns, 16th Paris December Finance Meeting, HKUST Finance Symposium, 2019 RCFS/RAPS Conference at Baha Mar
Delegation bears an intrinsic form of uncertainty. Investors hire managers for their superior models of asset markets, but delegation outcome is uncertain precisely because managers’ model is unknown to investors. We model investors’ delegation decision as a trade-off between asset return uncertainty and delegation uncertainty. Our theory explains several puzzles on fund performances. It also delivers asset pricing implications supported by our empirical analysis: (1) because investors partially delegate and hedge against delegation uncertainty, CAPM alpha arises; (2) the cross-section dispersion of alpha increases in uncertainty; (3) managers bet on alpha, engaging in factor timing, but factors’ alpha is immune to the rise of their arbitrage capital - when investors delegate more, delegation hedging becomes stronger. Finally, we offer a novel approach to extract model uncertainty from asset returns, delegation, and survey expectations.
ASU Sonoran Winter Finance, CEPR ESSFM Gerzensee, CUHK, European Winter Finance Summit, Geneva Workshop on Financial Stability, INSEAD, 2019 MFA, Stanford SITE, University of Zurich