A Study on the Relationship Between Executive Compensation and Corporate Earnings Manipulation Incentives

Posted: Mar 20, 2021

• Isabella Nelson
• Isabella Rodriguez
• Isabella Thomas

Abstract

This research investigates the complex relationship between executive compensation structures and corporate earnings manipulation incentives through an innovative computational framework that combines natural language processing, behavioral economics, and machine learning. Unlike traditional financial studies that rely on static regression models and established accounting metrics, our approach develops a dynamic multi-agent simulation environment that models executive decision-making under various compensation scenarios. We introduce a novel Earnings Manipulation Propensity Index (EMPI) that incorporates both quantitative financial indicators and qualitative textual analysis of corporate disclosures. Our methodology employs transformer-based language models to detect subtle linguistic patterns in earnings calls and financial reports that may indicate manipulation tendencies. The research examines compensation structures across 500 Fortune 1000 companies from 2015-2023, analyzing how different incentive components—including stock options, performance-based bonuses, and long-term incentives—correlate with manipulation behaviors. Our findings reveal a non-linear relationship where moderate performance-based compensation reduces manipulation incentives, while excessive equity-based compensation creates perverse incentives for short-term earnings management. The study contributes to the literature by providing a computational framework that can dynamically simulate compensation policy changes and predict their impact on corporate reporting behaviors, offering practical tools for boards and regulators to design compensation packages that align executive interests with long-term corporate integrity.