Category Archives: Accounting & Finance

Who pays attention to sustainability reports and why? Evidence from Google search activity

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Abstract

We introduce country-level Google search activity as a direct measure of the level of stakeholder attention directed towards sustainability reports. We validate this measure by establishing that search activity for sustainability reports is correlated with temporal patterns in firms' supply of these reports. To frame the economic magnitude of this search behaviour, we then show that the level of attention directed towards sustainability reports is very low compared to the level of attention directed towards financial and accounting information. Next, we examine two related research questions. First, we identify who pays attention to sustainability reports. We find, consistent with the environmental Kuznets curve, that attention towards sustainability reports is strongly associated with economic development. Consistent with findings in prior research that suggest citizens in stakeholder-oriented countries have stronger preferences for firms to act prosocially, we also find that search activity for sustainability reports relative to search activity for financial performance metrics is greater in civil law countries than in common law countries. Finally, we then explore the question of why individuals pay attention to sustainability reports and find evidence that suggests sustainability reports are used for two primary purposes: evaluating the societal impacts of firms' actions; and, firm valuation.

Enhancing bank transparency: Financial reporting quality, fraudulent peers and social capital

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Abstract

This study examines the role of social norms in financial markets by relating bank transparency to social capital. Using comprehensive data on commercial banks, we provide empirical evidence that high social capital contributes to more transparent financial reporting, thereby enabling more precise risk assessments and promoting financial stability. We find that the effect of social capital is more pronounced when commercial banks are more complex and disclosure incentives of bank managers are strong. Our results suggest that more opaque reporting by peers explains lower transparency but financial misreporting is less contagious when social capital is high. Our study suggests that social capital can effectively improve reporting transparency when other mechanisms are not effective, thus securing financial system stability.

Firm culture and internal control system

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Abstract

The corporate culture within firms is a significant concern for regulators, shareholders and other stakeholders. Drawing on a large sample of US firms, we use the political preferences of the top management team (TMT) to proxy for a firm's culture and examine whether it influences the decision to implement an effective internal control system (ICS) and whether the ICS plays a mediating role between the culture created by the TMT and financial reporting quality. We find that a Republican-leaning TMT with a more conservative ideology is associated with a more effective internal control system. In addition, the TMT's political preferences affect financial reporting quality, both directly and indirectly, via the internal control system. A range of robustness tests reinforces our main findings.

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

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Abstract

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.