Research Interests

  1. Empirical Asset Pricing (ex., stock return predictability),
  2. Regulatory Enforcement (ex., SEC enforcement), and
  3. Machine Learning in Finance.


Boca Corporate Finance and Governance Conference (Scheduled: 2022), University of Alabama (Scheduled: 2022), University of Missouri (2022*2, 2020), SFA (2021), World Finance Conference (2021), Crowell Prize Seminar (2021), AFA (2021 Ph.D. Poster), University of Miami Winter Conference on Machine Learning and Business (2021)


Crowell Prize 2020 (Third Prize: $2000), PanAgora Asset Management

Working Papers

  1. 150 Years of Return Predictability Around the World: A Holistic View Across Assets, Bai (2022)

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    Abstract: With new annual data of 16 developed countries across bond, equity, and housing markets, I study the return predictability using the payout-price ratios, i.e., coupon price, dividend price, and rent price. None of the 48 country-asset combinations shows consistent in-sample and out-of-sample performance with positive utility gain for the mean-variance investor. Only 3 (4/2) countries show positive economic gains in their equity (housing/bond) markets. The return predictability for the representative agents’ risky asset portfolios and wealth portfolios is even weaker, suggesting that timing the investment return of a country using payout-price ratios will not make the investors better off. The predictive regressions based on the VAR analysis by Cochrane (2008, 2011) suggest that 14 (5) countries have predictable payout growth in the equity (housing) markets, ex., the dividend price predicts the dividend growth in the US. The VAR simulation using data from all the countries does not reject the null that the dividend growth is predictable. This paper presents firm evidence against the return predictability based on payout ratios.

    VAR Simulation for the Equity and the Housing Markets Yang Bai (yangbai@mail.missouri.edu)

  3. Machine Learning Classification Methods and Portfolio Allocation: An Examination of Market Efficiency, Bai and Pukthuanthong (2020)

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    Presentations: SFA (2021), World Finance Conference (2021), AFA Ph.D. Poster Session (2021), Crowell Prize 2020 Seminar (2021), University of Miami Winter Conference on Machine Learning and Business (2021), University of Missouri (2020)

    Awards: Crowell Prize 2020 (Third Prize: $2000), PanAgora Asset Management

    Abstract: We frame the asset pricing problem as a machine learning classification problem. The predictions on 3.34 million observations yield significant out-of-sample economic gains. Through directly measured accuracies, binomial tests suggest that the classifiers can extract forward-looking contents from historical information, implying imperfect information efficiency. The classifiers exploit the differences in return state transition uncertainties. As reflected by a pre-realization measure based on multi-class predicted probabilities, the classifiers are more confirmative in predicting high-trading-friction stocks. Consistently, only trading frictions contribute to out-of-sample predictability throughout 26,302 distinct stocks’ lifetimes. The adjustment of the classifiers’ favorance over certain return states increases the performance.

    10-Minute Audio Introduction:

  5. Females Look Innocent in the Eyes of SEC, Bai and Guo (2022)

  6. Presentations: Boca Corporate Finance and Governance Conference (Scheduled: 2022), University of Alabama (Scheduled: 2022), University of Missouri (2022)

    Abstract: We investigate whether SEC enforcement and private investigations are affected by differential enforcement for firms with gender-diverse boards. Consistent with prior evidence in the legal literature regarding disparities in police and judicial enforcement, a one-standard-deviation increase in the percentage of females on board will reduce SEC enforcement risk by 0.3%, which is 27.3% of average SEC enforcement risk. We show that the reduced SEC enforcement is driven by issues in the SEC’s decision to initiate investigations. Compared to a board with less female directors, a board with more than 30% female directors is 1.2% less likely to be subject to investigation by the SEC. The subsequent analysis fails to support a reverse causality conjecture that female directors can reduce financial abnormalities.