How to detect and forecast corporate fraud by media reports? An approach using machine learning and qualitative comparative analysis

Abstract

AbstractThe media plays an important role in detecting corporate financial fraud. However, little systematic research exists on the impact of media reports on corporate fraud detection; thus, our understanding of the impact is limited. Therefore, we are committed to determining how the configuration of different media report content systematically detects corporate fraud by logistical regression, grounded theory and qualitative comparative analysis (QCA). First, the media reports are classified into three major categories and 35 subclasses to determine their features through fraud triangle theory and grounded theory. Then, based on a dataset of 110 fraudulent listed companies and 110 matched listed companies from 2010 to 2020, three major features comprising 10 subclasses are identified by the logistical regression method. The causal configurations of the features of media reports that detect corporate fraud are explored using the QCA method. The results show that five particular associations can interpret corporate fraud revelation by meeting the equifinality and asymmetric causality principles. Finally, the combined model is proposed. Through 56 fraudulent listed companies and 56 matched listed companies from 2021 to 2022, the combined model is proven to be most effective in detecting corporate fraud. In summary, we offer theoretical contributions to corporate fraud detection and empirical experiences for corporate managers and regulators.