Investigating the Influence of External Auditors on the Mitigation of Earnings Manipulation in Listed Companies

Posted: Apr 25, 2014

• Logan White
• Lucas Clark
• Lucas Nelson

Abstract

This research presents a novel computational framework for analyzing the complex relationship between external auditors and earnings manipulation in publicly listed companies. Unlike traditional accounting studies that rely on conventional statistical methods, we introduce a hybrid approach combining quantum-inspired optimization algorithms with federated learning techniques to model auditor effectiveness across distributed financial datasets. Our methodology enables the identification of subtle patterns in earnings management behaviors while preserving data privacy across multiple institutions—a critical consideration given the sensitive nature of financial information. We developed a unique auditor effectiveness metric that incorporates both quantitative financial indicators and qualitative governance factors, processed through a bio-inspired neural network architecture. The analysis of 1,250 publicly traded companies over a five-year period reveals that auditor characteristics such as industry specialization, technological capability, and audit committee engagement patterns significantly influence the detection and prevention of earnings manipulation. Our findings demonstrate that quantum-enhanced clustering algorithms can identify previously undetected patterns of auditor effectiveness that traditional methods overlook. The research contributes to both accounting literature and computational finance by introducing innovative analytical techniques and providing empirical evidence of how specific auditor attributes correlate with reduced instances of earnings management. This cross-disciplinary approach bridges computational intelligence with financial regulation, offering new insights for regulators, investors, and auditing firms seeking to enhance financial reporting quality.