We examine relationship between common institutional ownership and corporate social responsibility (CSR). We find that common institutional ownership is negatively associated with the level of CSR, which supports an anti-competitive view. We conduct a propensity score matching (PSM) analysis and a difference-in-differences (DiD) analysis based on a quasi-natural experiment of financial institution mergers. The results alleviate concerns about endogeneity. Using the DiD setting, we find further support for the anti-competitive view, and can rule out alternative explanations. Additional analyses on investor characteristics show that our results come mainly from common owners with long-term investment horizons or lower social inclination. Moreover, we find that the anti-competitive effect is more pronounced for mature firms, and for firms in industries with lower labor intensity and lower customer sensitivity.

Using machine learning and many predictors, we find strong bond return predictability, with an out-of-sample R-squared of 4.48% and an annualized Sharpe ratio of 3.27. ML models identify important predictors for aggregate predictors (bond market returns, TERM and HML factors, GDP growth) and bond characteristics (downside risk, short-term reversal, return skewness, and credit spreads). Predictability varies over time, being stronger during periods of high investor risk aversion, slow economic growth, and strong cross-sectional factor explanatory power. Our results highlight the benefits of leveraging both cross-sectional and time-series predictors to forecast corporate bond returns while considering public and private bonds.

We introduce a new class of tree-based models, P-Trees, for analyzing (unbalanced) panel of individual asset returns , generalizing high-dimensional sorting with economic guidance and interpretability. Under the mean–variance efficient framework, P-Trees construct test assets that significantly advance the efficient frontier compared to commonly used test assets, with alphas unexplained by benchmark pricing models. P-Tree tangency portfolios also constitute traded factors, recovering the pricing kernel and outperforming popular observable and latent factor models for investments and cross-sectional pricing. Finally, P-Trees capture the complexity of asset returns with sparsity, achieving out-of-sample Sharpe ratios close to those attained only by over-parameterized large models.