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
The Impact of Beliefs on Credit Markets: Evidence from Rating Agencies
An open question in finance and economics is how the beliefs of agents in the economy affect the credit cycle and real economic activity. We analyze the impact of beliefson credit market conditions in the context of credit rating agencies (CRAs). We measure CRAs’ subjective beliefs as the difference in forecasts of future aggregate creditspreads between CRAs and a consensus of other financial institutions. When CRAsare relatively more optimistic, they issue higher credit ratings even though their forecasts do not contain additional information regarding future credit market conditions.CRA optimism leads to lower initial yields and subsequent negative returns for newlyissued bonds. In response to this mispricing, firms increase their debt, leverage, andinvestment, where the effects are most pronounced among rated firms. Overall, ouranalysis shows how beliefs drive aggregate financing and investment behavior 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
Flow-Based Asset Pricing: A Factor Framework of Cross-sectional Price Impacts
Financial Markets and Corporate Governance Conference Runner-up for Best Paper
We study how noisy flows impact the prices of the cross-section of assets, particularly through the interaction between the factor structure of flows and the assets’ risk structure. In our new framework, systematic flows into systematic risk factors generate a factor model of price impacts. We develop empirical methods for the model by introducing flows into classical portfolio tools, including the Sharpe ratio, Fama-MacBeth regression, Fama-French portfolios, and Gibbons-Ross-Shanken test. We estimate the model using U.S. equity mutual fund flows data. The model-implied strategy that optimally profits from flows improves the investment performance of most existing characteristics-based anomaly portfolios.
Notre Dame, Johns Hopkins Carey, RUC-VUW Joint Virtual Research Workshop, Wolfe Research 6th Annual Wolfe Global Quantitative and Macro Investment Conference, Federal Reserve Board, Campbell & Company, MFA Annual Meeting, Southern Methodist University, FMCG 2023, SoFiE 2023 Conference
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