This study focuses on a traditional business ethics question and aims to use advanced techniques to improve the performance of corporate fraud prediction. Based on the GONE framework, we adopt the machine learning model to predict the occurrence of corporate fraud in China. We first identify a comprehensive set of fraud-related variables and organize them into each category (i.e., Greed, Opportunity, Need, and Exposure) of the GONE framework. Among the six machine learning models tested, the Random Forest (RF) model outperforms (...) the other five models in corporate fraud prediction. Based on the RF model, we show that Exposure variables play a more important role in predicting corporate fraud than other input variables. These results highlight the importance of Exposure variables in corporate fraud prediction and promote the practical use of the machine learning model in solving business ethics questions. (shrink)

As global warming has received widespread attention, the disclosure of firms’ carbon information has been expected by increasing stakeholders. This study extends the previous literature on the determinants of firms’ carbon information disclosure by examining the influence of academic independent directors and Confucianism on the quality of carbon information disclosure. Using a sample of Chinese listed firms in the CSI 300 Index during the period of 2012–2018, our empirical results show that academic independent directors have a significantly positive association with (...) the quality of carbon information disclosure. The results also reveal that Confucianism positively affects the quality of carbon information disclosure. Moreover, Confucianism positively moderates the relationship between academic independent directors and the quality of carbon information disclosure. The results imply that Confucianism, as an informal system, can promote the governance effect of academic independent directors on firms’ carbon information disclosure behaviour. Our findings offer shareholders, regulators, and other stakeholders an integrating perspective on motivating firms to disclose high quality carbon information. (shrink)

The relationship between analysts' forecasts and corporate fraud is a vital theoretical and practical question that needs to be clarified. Based on a strict distinction between negative performance gaps relative to analyst forecasts (negative forecast gaps hereinafter) and analyst coverage, this study investigates the influence of analyst forecasts on corporate fraud from a panoramic perspective. Using panel data on listed companies in China from 2008 to 2019, we find that short-term performance pressure caused by negative forecast gaps is significantly positively (...) correlated with firms’ possibility for fraudulent behavior. However, this positive correlation is weaker in state-owned enterprises than in their non-state-owned counterparts. Further, as a key external governance mechanism, analyst coverage enhances the negative moderator effect of state ownership on the positive relationship between negative forecast gaps and the likelihood of corporate fraud. (shrink)