## Chen Wang

### chen.wang@nd.edu

I am an assistant professor of finance at the Mendoza College of Business, University of Notre Dame. My current research focuses on subjective beliefs $\mathbb{E}^S(X_{t+h}|\mathcal{I}_t)$:

• how people form their beliefs on economic variables
• how these beliefs impact asset prices and other equilibrium outcomes

### Interests

• Asset Pricing
• Behavioral Finance

### Education

• Ph.D. in Financial Economics, 2020

Yale School of Management

• M.S. in Financial Economics, 2014

• B.A. in Finance, 2012

Peking University, Guanghua School of Management

# Research

Working Papers

Factor Demand and Factor Returns (Apr, 2021)
Abstract: We study the asset-pricing implications of mutual funds’ factor demand. Demand for a pricing factor, measured by loadings of fund returns on factor returns, is highly persistent at the fund level, generating a rebalancing motive when stock characteristics are time-varying. This phenomenon of ‘factor rebalancing’ leads to predictable trading and return predictability in the cross-section. When there is a ‘mismatch’ between stocks and the underlying funds’ factor demand—e.g., growth stocks held by value funds—‘mismatched’ stocks will face more selling pressure and subsequently earn lower returns. Double-sorting on stock characteristics and mutual fund factor demand refines value and momentum strategies, generating abnormal returns that cannot be explained by subsequent fundamentals or retail trading flows.
Presentations: 2019 RCFS/RAPS Conference at Baha Mar, 2019 CICF, 2019 SGF, 2021 AFA, 2021 MFA; Cambridge, LSE, Notre Dame, USI Lugano, Yale
Under- and Over-Reaction in Yield Curve Expectations (Mar, 2020)
Abstract: I study how professional forecasts of interest rates across maturities respond to new information. I document that forecasts for short-term rates underreact to new information while forecasts for long-term rates overreact. I propose a new explanation based on autocorrelation averaging,’’ whereby, due to limited cognitive processing capacity, forecasters’ estimate of the autocorrelation of a given process is biased toward the average autocorrelation of all the processes they observe. Consistent with this view, I show that forecasters over-estimate the autocorrelation of the less persistent term premium component of interest rates and under-estimate the autocorrelation of the more persistent short rate component. A calibrated model quantitatively matches the documented pattern of misreaction. Finally, I explore the pattern’s implication for asset prices. I show that an overreaction-motivated predictor, the realized forecast error for the 10-year Treasury yield, robustly predicts excess bond returns.
Presentations: 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
Rediscover Predictability: A Duration-Based Approach (Dec, 2019)
16th Paris December Finance Meeting Best Paper Award
Abstract: 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.
Presentations: LBS Trans-Atlantic Doctoral Conference, Yale SOM, Econometric Society Annual Meeting, NFA Annual Meeting, 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 Uncertainty (Jan, 2019)
Abstract: 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.
Presentations: ASU Sonoran Winter Finance, CEPR ESSFM Gerzensee, CUHK, European Winter Finance Summit, Geneva Workshop on Financial Stability, INSEAD, MFA Annual Meeting, Stanford SITE, University of Zurich

Keywords