Abstract

Purpose: This research delves into the nuanced interconnections between the profit management strategies employed by corporations in Saudi Arabia and the corresponding corporate governance protocols, providing valuable insights for relevant stakeholders. The study employs an expansive model integrating sophisticated machine learning techniques with regression analysis to scrutinize the impact of governance frameworks on the manipulation of earnings over a temporal continuum, utilizing financial data sourced from a diverse array of Saudi Arabian enterprises. Method: This investigation utilizes a robust methodological approach that amalgamates advanced machine learning, incorporating artificial neural networks, with conventional regression analysis. Through the examination of temporal financial data from a heterogeneous sample of Saudi Arabian companies, the study elucidates non-linear patterns and intricate relationships, particularly within variables such as ownership concentration. Findings: The investigation discloses an inverse correlation between earnings management and board independence, underscoring the pivotal contribution of independent boards in augmenting financial transparency. Conversely, a positive correlation between CEO duality and earnings management implies the intricate dynamics inherent in a conjoined CEO-board chair role. This study furnishes nuanced insights into the intricacies characterizing the corporate governance and financial mechanisms of Saudi Arabian corporations. Implications/Originality: The results proffer pragmatic suggestions for fortifying governance, investment, and audit protocols, with implications for policymakers, corporate boards, investors, and auditors. Underscoring the importance of autonomous boards and circumspect CEO duality, the comprehensive methodology employed in this study, encompassing both traditional and innovative methodologies, enriches the comprehension of the intricate corporate governance dynamics within Saudi Arabian entities.