Preprint.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: This paper presents a proactive model that anticipates future misreporting by examining early indicators leading up to the misreporting event. Specifically, we leverage the slippery slope phenomenon (SS), characterized by escalating aggressive accounting practices, and use Benford Law (BL) to detect such behaviour. Our findings show misreporting firms exhibit a significant and enduring pattern of increasing aggressive reporting pre-misreporting. Utilizing a proportional hazard model, our approach anticipates (out-of-sample) 80.2% future misreporting 4.65 years in advance, with 26.4% Type I error rate. Additionally, integrating the SS pattern into current fraud prediction models significantly improves their ability to identify the year of fraud, as reflected by a 24% increase in the AUC out-of-sample. This anticipatory model provides stakeholders with early warnings for intervention, potentially reducing misreporting costs and maintaining capital markets trust. We contribute by introducing the SS concept, employing BL effectively and developing an unbiased model with direct practical applicability.
Bibtex:
@misc{,
author = {Joanne Horton and Dhanya Krishna Kumar and Facundo Mercado},
title = {Anticipating Corporate Misreporting: Leveraging the Slippery Slope Phenomenon and its Predictive Power},
year = {2023},
url = {https://www.bayes.city.ac.uk/__data/assets/pdf_file/0008/754577/Dhanya_Krishna_Kumar_.pdf},
}
Reference Type: Preprint
Subject Area(s): Accounting