Chen Wang

Assistant Professor of Finance

Mendoza College of Business

University of Notre Dame



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) $ and empirical asset pricing.

Recent updates


  • Asset Pricing
  • Behavioral Finance
  • Macro Finance


  • Ph.D. in Financial Economics, 2020

    Yale School of Management

  • M.S. in Financial Economics, 2014

    Columbia Business School

  • B.A. in Finance, 2012

    Peking University, Guanghua School of Management


Working Papers

Slicing an Asset to Learn about Its Future: A New Perspective on Return and Cash-Flow Forecasting (2024)
Slicing an asset by payout horizons unseals information about its future returns and cash flows. As an example, we slice an equity market index into granular pieces (dividend strips) and show that valuation ratios of its strips span the underlying state variables of the index. Strip valuation ratios form a term structure. The level and slope strongly predict the index dividends. The slope alone is sufficient for forecasting the index return. The steepening and flattening of valuation term structure reflect discount-rate variations rather than information on the cash-flow trajectory, because market participants have very limited information about long-term cash flows.


The Impact of Beliefs on Credit Markets: Evidence from Rating Agencies (2024)
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.

PDF SSRN Presentation Video

Categorical Thinking about Interest Rates (2024)
Rational expectations imply that the current long-term interest rate should already incorporate public knowledge of anticipated increases in short rates. Yet, there is a widespread misconception that expected future shifts in the short rate forecast corresponding future movements in the long rate. We hypothesize that people lump short- and long-term interest rates into the coarse category of ``interest rates,’’ leading to overestimation of their comovement. We show that categorical thinking about interest rates is evident even among professional forecasters and distorts the real behavior of borrowers and investors. Expectations of rising short rates prompt homebuyers and firms to rush to lock in long-term debt before further increases in long rates, reducing the effectiveness of monetary policy. Investors are also less willing to hold long-term bonds because they anticipate future increases in long rates. The increase in supply and decrease in demand for long-term debt cause long rates to overreact to changes in short rates, and can help explain the excess volatility puzzle in long rates.


Pre-Refunding Announcement Gains in U.S. Treasurys (2024)
Quantpedia Awards 2024 – 1st Place
Each quarter, the Treasury Department unveils its refunding plan, detailing the following quarter’s treasury issuances in terms of size and maturity composition. We document substantial positive returns on long-term Treasurys on the day before these Treasury Refunding Announcements (TRAs), a pattern persisting since the 1990s and intensifying over the last two decades amidst growing Federal deficits. These pre-TRA gains are distinct from known end-of-month pricing patterns and account for a sizable fraction of annual yield and term premium changes. Implementing a trading strategy focused solely on these four days per year yields a Sharpe ratio of over 4. We provide evidence of uncertainty reduction and associated information production around TRAs as a potential mechanism. Finally, we discuss implications for some documented bond market patterns and the pre-FOMC drift in the equities market.


A Factor Framework for Cross-Sectional Price Impacts (2024)
Financial Markets and Corporate Governance Conference Runner-up for Best Paper
We study how noise trading flows impact the cross-section of asset prices in a market where sophisticated investors enforce no-arbitrage. In our model, individual asset flows, aggregated at the factor level, drive fluctuations in factor risk premia, which in turn impact asset prices through beta pricing. This structure fits the reduced-form patterns of how each asset’s flow impacts its own price and other assets’ prices with only a few factor-level parameters. A model-implied trading strategy, designed to exploit the reversion of factor-level price impacts, delivers strong investment outcomes and improves the performance of a wide range of anomaly portfolios.


Factor Rebalancing (2023)
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.


Under- and Overreaction in Yield Curve Expectations (2021)
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.

PDF Online Appendix SSRN

Rediscover Predictability: Information from the Relative Prices of Long-term and Short-term Dividends (2019)
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.


Delegation Uncertainty (2019)
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.