Hongye Guo

• Hongye Guo and Jessica Wachter, Correlation neglect in asset prices. Abstract

  The U.S. stock market return during the first month of a quarter positively predicts the second month’s return, which in turn negatively predicts the first month’s return of the next quarter. This pattern arises because investors fail to fully recognize that earnings announced in the second month of a quarter are inherently similar to those announced in the first month, leading them to overreact to predictably repetitive earnings news. A model formalizing this form of correlation neglect yields additional predictions for survey data and for both the time-series and cross-section of returns, all of which are borne out in the data. These results provide evidence of correlation neglect even among sophisticated, financially incentivized decision-makers, underscoring its importance as a behavioral phenomenon.

• Hongye Guo and Jessica Wachter (2025), “Superstitious” Investors, Review of Asset Pricing Studies, 15 (1), pp. 1-45. Related
  Links

  Working paper version
• Hongye Guo and Jessica Wachter (Working), Forecast-agnostic portfolios. Abstract

  We introduce forecast-agnostic (FA) portfolios that exhibit out-of-sample market-timing ability without relying on estimated predictive coefficients. These portfolios go long or short the market based on the level of a predictor variable, thereby avoiding the instability and estimation error that undermine traditional market-timing strategies. Despite using predictor variables that typically deliver negative out-of-sample R2 values (Goyal et al., 2024), FA portfolios deliver significantly positive alphas on average. We explain these seemingly contradictory phenomena by interpreting regression coefficients as portfolio returns: genuine predictability is necessary for high portfolio returns, whereas achieving a positive out-of-sample R2 additionally requires the ability to forecast the returns on the forecast-agnostic portfolios themselves. As these FA portfolio returns could not be too predictable, estimating them substantially penalizes the out-of-sample R2 by the inverse of the estimation sample size. Simulations show that the statistic we propose has power to detect predictability that extends beyond in-sample diagnostics and the out-of-sample R2.

• Hongye Guo, Hengjie Ai, Ravi Bansal, Amir Yaron (Working), Identifying preference for early resolution from asset prices.
• Winston Wei Dou, Hui Chen, Hongye Guo, Yan Ji (Working), Feedback and Contagion through Distressed Competition.
• Hongye Guo (Working), Earnings Extrapolation and Predictable Stock Market Returns. Abstract

  The U.S. stock market’s return during the first month of a quarter correlates strongly with returns in future months, but the correlation is negative (positive) if the future month is (is not) the first month of a quarter. These effects offset, leaving the market return with its weak unconditional predictive ability known to the literature. The pattern accords with a model in which investors extrapolate announced earnings to predict future earnings, not recognizing that earnings in the first month of a quarter are inherently less predictable than in other months. Survey data support this model, as does out-of-sample evidence across industries and international markets. These results seriously challenge the Efficient Market Hypothesis and advance a novel mechanism of expectation formation.

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