Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China

The current research aims to launch effective accounting fraud detection models using imbalanced ensemble learning algorithms for China A-Share listed firms. Based on a sample of 33,544 Chinese firm-year instances from 1998 to 2017, this research respectively established one logistic regression and four ensemble learning classifiers (AdaBoost, XGBoost, CUSBoost, and RUSBoost) by 12 financial ratios and 28 raw financial data. Additionally, we divided the sample into the train and test observations to evaluate the classifiers' out-of-sample performance. In detail, we applied two metrics, namely, Area under the ROC (receiver operating characteristic) curve (AUC) and Area under the Precision-Recall curve (AUPR), to evaluate classifiers' discriminability. In the supplement test, this study put forward an algebraic fused model on the basis of the four ensemble learning classifiers and introduced the sliding window technique. The empirical results showed that the ensemble learning classifiers can detect accounting fraud for the imbalanced China A-listed firms far more effectively than the logistic regression model. Moreover, imbalanced ensemble learning classifiers (CUSBoost and RUSBoost) effectively performed better than the common ensemble learning models (AdaBoost and XGBoost) in average. The algebraic fused model in the supplement test also obtained the highest average AUC and AUPR among all the employed algorithms. Our results offer firm support for the potential role of Machine Learning (ML)-based Artificial Intelligence (AI) approaches in reliably predicting accounting fraud with high accuracy. Similarly, for the Chinese settings, our ML-based AI offers utmost advantage in forecasting accounting fraud. Finally, this paper fills the research gap on the applications of imbalanced ensemble learning in accounting fraud detection for Chinese listed firms.     

Consumer ethics: A review and research agenda

Consumer ethics continues to draw the attention of academicians and practitioners as a significant economic and social issue globally. Consumer ethics refers to moral principles that govern a consumer's behaviour. This literature review seeks to enrich the discourse on consumer ethics through a comprehensive and detailed review of 106 articles, covering 21 journals from 2010 to 2020. Through an examination of theories, contexts, characteristics, and methodologies used in consumer ethics research, our review (1) presents a comprehensive and up-to-date overview of the research in this field and (2) sets a future research agenda to spur scholarly research. We found studies have primarily relied on a single theoretical lens such as the theory of marketing ethics, planned behaviour, and neutralization theory. Further consumer ethics research focuses on advanced countries, with a narrow focus on developing countries. We have diagnosed the need to examine boundary conditions impacting consumer ethics. Finally, we provide actionable inputs to combat unethical consumer actions as well as promote ethical consumption.     

IRS Private Letter Rulings: Initial Evidence on Determinants and Consequences

This study examines the determinants of firms' requests for Private Letter Rulings (PLRs) from the US Internal Revenue Service (IRS) and their impact on firms' cash holdings. Our results show that PLR requests tend to be made by firms with more active tax planning, more acquisitions, higher analyst following, higher leverage, and less in-house tax expertise. We also show that firms with IRS audit red flags are less likely to request a PLR. We use a difference-in-difference approach to assess changes in cash holdings following PLR requests and report a decrease in cash holdings for PLR firms, consistent with the notion that PLRs act to reduce tax uncertainty. Our study provides the first empirical evidence about the determinants of PLR requests and complements prior work on tax uncertainty and cash holdings (Hanlon, Maydew and Saavedra, 2017).