A Computational Textual Assurance Framework for Enhancing Extended Audit Reporting Quality: Evidence from Emerging and Advanced Capital Markets

Abstract : Purpose and Design: This study develops a computational textual assurance framework to enhance the quality of extended audit reporting in capital markets. Motivated by the persistent reliance on short-form audit reports in several emerging economies, the framework aims to strengthen narrative transparency, communicative value, and investor-relevant disclosures through advanced linguistic and algorithmic textual analytics. Methodology/Approach: Drawing on a cross-market sample comprising emerging and advanced capital markets, the study applies a multi-layer computational textual architecture that integrates quantitative text-mining, linguistic assurance indicators, and narrative-quality scoring models. The framework extracts, classifies, and evaluates extended audit reporting components—including key audit matters, risk explanations, judgments, materiality, and auditor–client interactions—using automated narrative diagnostics. Findings: The results demonstrate that the proposed framework significantly improves the informativeness, depth, and comparability of extended audit reports. Reports enhanced through the framework show higher coherence, richer disclosure patterns, and stronger explainability of audit judgments. The improvement is more pronounced in emerging markets, where traditional reporting practices remain narrow. Originality and Value: The study offers one of the first cross-market empirical examinations of extended audit reporting quality using computational textual assurance. It contributes to audit disclosure theory by operationalizing narrative quality through measurable linguistic indicators rather than subjective auditor judgment. Practically, the framework provides regulators, audit firms, and listed companies with a scalable mechanism for improving extended audit reporting without altering the core audit process. Theoretical, Practical, and Societal Implications: The findings inform ongoing regulatory debates in emerging economies—such as Egypt—about shifting from short-form audit reports toward internationally aligned extended reporting models to enhance transparency, investor protection, and market credibility.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-07
DOI
https://doi.org/10.5281/zenodo.18171457
Primary Topic
Auditing, Earnings Management, Governance
Type
article
Field-Weighted Citation Impact
0.00
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article

A Computational Textual Assurance Framework for Enhancing Extended Audit Reporting Quality: Evidence from Emerging and Advanced Capital Markets

Lotfy Amin ElSayed Ahmed
Zenodo (CERN European Organization for Nuclear Research)
Auditing, Earnings Management, Governance
article

A Computational Textual Assurance Framework for Enhancing Extended Audit Reporting Quality: Evidence from Emerging and Advanced Capital Markets

Lotfy Amin ElSayed Ahmed
article en

Abstract

Abstract : Purpose and Design: This study develops a computational textual assurance framework to enhance the quality of extended audit reporting in capital markets. Motivated by the persistent reliance on short-form audit reports in several emerging economies, the framework aims to strengthen narrative transparency, communicative value, and investor-relevant disclosures through advanced linguistic and algorithmic textual analytics. Methodology/Approach: Drawing on a cross-market sample comprising emerging and advanced capital markets, the study applies a multi-layer computational textual architecture that integrates quantitative text-mining, linguistic assurance indicators, and narrative-quality scoring models. The framework extracts, classifies, and evaluates extended audit reporting components—including key audit matters, risk explanations, judgments, materiality, and auditor–client interactions—using automated narrative diagnostics. Findings: The results demonstrate that the proposed framework significantly improves the informativeness, depth, and comparability of extended audit reports. Reports enhanced through the framework show higher coherence, richer disclosure patterns, and stronger explainability of audit judgments. The improvement is more pronounced in emerging markets, where traditional reporting practices remain narrow. Originality and Value: The study offers one of the first cross-market empirical examinations of extended audit reporting quality using computational textual assurance. It contributes to audit disclosure theory by operationalizing narrative quality through measurable linguistic indicators rather than subjective auditor judgment. Practically, the framework provides regulators, audit firms, and listed companies with a scalable mechanism for improving extended audit reporting without altering the core audit process. Theoretical, Practical, and Societal Implications: The findings inform ongoing regulatory debates in emerging economies—such as Egypt—about shifting from short-form audit reports toward internationally aligned extended reporting models to enhance transparency, investor protection, and market credibility.

Zenodo (CERN European Organization for Nuclear Research)
Beni-Suef University (EG)
Openalex Percentile: Top 96%
Auditing, Earnings Management, Governance
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