Designing an Intelligent Model for Assessing Earnings Quality with Emphasis on Accruals, Earnings Persistence, and Earnings Manipulation Patterns
Keywords:
Earnings quality, accruals, earnings persistence, earnings management, earnings manipulation, intelligent model, financial reporting.Abstract
This study aimed to design an intelligent model for assessing earnings quality by focusing on accrual quality, earnings persistence, cash-flow predictability, and patterns of earnings manipulation. This study employed a qualitative exploratory design. The participants consisted of 19 experts in financial accounting, auditing, capital market analysis, financial management, and academic accounting from Tehran, Iran. Participants were selected through purposive sampling. Data were collected using semi-structured interviews, and the interview process continued until theoretical saturation was achieved. Thematic analysis was applied to analyze the data, and NVivo software was used to organize, code, compare, and refine the qualitative evidence. Coding was performed at three levels: initial codes, subcategories, and main categories. To enhance trustworthiness, member checking, peer review, analytical memoing, and audit trail documentation were used. The analysis resulted in four main categories: accrual quality and reliability, earnings persistence and predictability, earnings manipulation patterns, and the data-driven architecture of an intelligent earnings quality assessment model. Participants emphasized that earnings quality should not be evaluated through a single accounting ratio or isolated financial indicator. Instead, an intelligent model should integrate accrual-based signals, cash-flow consistency, temporal stability, managerial incentives, audit evidence, and governance-related indicators. Abnormal discretionary accruals, persistent gaps between earnings and operating cash flows, unusual changes in provisions, abnormal credit sales, reduction of discretionary expenditures, income smoothing, and timing-based adjustments were identified as major warning signs of low earnings quality. The findings indicate that earnings quality is a multidimensional construct and that designing an intelligent model for its assessment requires the integration of financial, auditing, governance, and behavioral data. The proposed model can assist auditors, financial analysts, managers, and regulators in the early identification of earnings manipulation risk, improvement of financial reporting reliability, and enhancement of economic decisions based on more persistent and transparent earnings.
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