Flow-Based Asset Pricing
We analyze the flow-driven fluctuations of the cross section of asset prices. We take the stance that price impacts of uninformative flows arise as marginal investors’ risk compensation. We show that shorting the portfolio that incurs the maximum price impact for a given level of fundamental risk is the most efficient trading-against-flow strategy. To form this strategy, we build a new model of common factors of flows and common factors of fundamental returns and estimate the model using U.S. equity mutual fund flows into Fama-French three factors. Our strategy increases the out-of-sample annualized Sharpe ratio of 159 firm characteristics-based anomaly portfolios by an average of 0.3. Our evidence shows that risk-driven price impacts depend only on factor flows but not on idiosyncratic flows.
Notre Dame, Johns Hopkins Carey
Factor Demand and Factor Returns
CFAM-ARX Paper Award, Finance Down Under Conference, 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; Birkbeck (University of London), BlackRock, Cambridge, LSE, Notre Dame, Peking University, USI Lugano, Yale SOM
Rating Agency Beliefs and Credit Market Distortions
Credit rating agencies (CRAs) make regular forecasts of the future credit market conditions and explicitly incorporate these forecasts in their credit rating processes. We show that CRAs beliefs induce mispricing in corporate bond markets, which in turn affect firms’ financial and investment decisions. We propose a measure of CRA subjective beliefs as the difference in forecasts of future aggregate credit spreads between CRAs and a consensus from many other financial institutions. When CRAs are relatively more optimistic, they issue higher credit ratings despite their lack of additional information regarding future credit market conditions. CRA optimism leads to lower initial yields and subsequent negative returns for newly issued bonds. In response to this mispricing, firms increase their debts, leverage, and investments—where the effects are most pronounced among rated firms—and unrated firms are more likely to be rated.
Notre Dame, McGill, CICF 2022, AsianFA 2022
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