Prior research documents that ownership by multiple large shareholders (MLS) could alleviate agency conflicts between controlling shareholders and small shareholders through improved monitoring. We provide evidence of a “dark side” to MLS. Using a sample of Chinese listed firms during 2005–2014, we find a positive association between the presence of MLS and excess executive compensation. Furthermore, excess compensation is greater in firms in which the different types of large shareholders have relatively equal voting power. Overall, these results imply that coordination friction among MLS reduces large shareholders' monitoring efficiency and exacerbates agency problems between shareholders and executives.

In this study, we examine how achievement‐related tendencies are expressed in the professional auditing context, particularly through the interplay between the CEO and the audit partner. We use the facial width‐to‐height ratio (fWHR), a stable morphological trait widely applied in prior research, as a proxy for achievement drive. Using a sample of US audit partners from 2016 to 2019, we find that higher achievement drive is associated with enhanced audit quality, evidenced by fewer restatements and lower abnormal accruals. Auditors with higher achievement drive are also more likely to become industry experts, attain leadership positions, and achieve partnership status more quickly. Importantly, we find that high‐achievement‐drive audit partners are more inclined to assert dominance in negotiations, particularly when working with equally driven CEOs, leading to improved audit quality. Overall, our findings suggest that, when activated in auditing contexts, achievement‐oriented tendencies, as proxied by fWHR, are linked to higher audit quality.

This study employs a broad set of machine learning (ML) methods to examine cross-sectional variation in corporate bond returns in China. Using macroeconomic indicators together with bond- and issuer-specific characteristics, we find that ML techniques outperform traditional linear models in both statistical and economic terms. These models are particularly effective at capturing distinctive features of the Chinese market, including the dominance of state-owned enterprises, implicit government guarantees, and rapid market evolution. We compare long-short and long-only portfolio strategies to account for practical constraints on short selling. The results indicate that ML methods are effective in markets where institutional features and information asymmetries play a central role in asset pricing.