Deception Detection in Earnings Conference Calls:a Discourse Analytical Approach

The 2001 Enron scandal triggered concern for existing accounting regulations and raised awareness of corporate fraud. Since, new rules and legislation have been implemented that emphasise the accuracy of financial reporting. Within the investment community, fraud has traditionally been detected through forensic accounting and fundamental equity analysis. More recently, however, linguistically informed approaches are becoming popular (Dille 2020; Hope & Wang 2018; Crawford Camiciottoli 2017; Larcker & Zakolyukina 2012). Many studies have developed word-counting approaches with the aim of detecting deception in the reporting genre, namely earnings conference calls, a form of financial disclosure provided by company management to the investment community. However, a gap was recognised in the literature in that how exactly fraudulent managers use words and why in order to deceive was yet to be fully recognised and addressed. This thesis addresses this gap. Detecting deception is challenging, with the results from previous experiments and studies all suggesting varied and conflicting linguistic correlates. I have developed a taxonomy of features obtained from the wider deception literature and applied a hybrid abductive discourse analytical approach to non-fraudulent company earnings calls and to ones where the Chief Executive Officer is known to have “cooked the books” and portray a deceptive image of company performance to investors. My findings focus on the relevance of context and how deception manifests through different linguistic techniques at varying levels of language description. Moreover, the findings provide empirical evidence of how deception may manifest in earnings calls, in that vast clusters of cues working together offer potential insight into deception and its detection. In other words, and confirming previous research, no single linguistic cue of deception can work alone in detecting deception. As the findings demonstrate, most/all cues more likely and exclusively associated with deception must be present, as well as cues more likely associated with non-fraudulent ECs must not be present when attempting to identify deceptive CEOs. A subsequent validation blind study confirms my findings. Overall, the findings create a foundation worthy of future research and development in deception detection and may be used and adapted for any future research in detecting deception in the context of earnings calls and language use. The findings have contributed to the wider deception knowledge and, more exclusively, to the knowledge of earnings calls, the reporting genre, and deception detection