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 (Jul, 2021)
Abstract: We show that mutual funds’ factor demand drives stock return predictability and explains why value and momentum prevail among certain stocks and fail among others. A fund’s factor demand, measured by the loadings of fund returns on factor returns, is highly persistent over time. Persistence in factor demand combined with time-varying stock characteristics generates a strong rebalancing motive—a phenomenon we term ‘factor rebalancing.’ Factor rebalancing leads to predictable trading, and the associated price pressure results in stronger value and momentum for stocks with characteristics well-matched with the underlying funds’ factor demand. Mismatched stocks, in contrast, face more selling pressure in the short run and experience lower returns. By quantifying the scale of factor rebalancing and its price impact, we estimate an average demand elasticity of -0.23.
Presentations: 2019 RCFS/RAPS Conference at Baha Mar, 2019 CICF, 2019 SGF, 2021 AFA, 2021 MFA, 2021 NFA; Birkbeck (University of London), BlackRock, Cambridge, LSE, Notre Dame, Peking University, USI Lugano, Yale SOM
Under- and Over-Reaction in Yield Curve Expectations (Jun, 2021)
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, 2021 WFA
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, 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 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, 2019 MFA, Stanford SITE, University of Zurich

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