Optimizing SEC Comment Letter Analysis with the CLARITY (Comment Letter Automated Retrieval and Intelligent Text Yielding) Process

ABSTRACT The CLARITY (Comment Letter Automated Retrieval and Intelligent Text Yielding) process offers a novel, automated approach for retrieving, cleaning, and analyzing U.S. Securities and Exchange Commission (SEC) comment letters. Addressing the challenges of volume, inconsistency, and accessibility, CLARITY retrieves letters from the EDGAR database spanning 2005–2024. Python-based tools ensure standardized data cleaning, robust error handling, and efficient processing. Unlike subscription-based platforms such as Audit Analytics (Audit Analytics 2024) or Intelligize (Intelligize 2024), CLARITY is open access, reproducible, and scalable. The resulting CLARITY dataset supports advanced analyses—such as sentiment analysis and longitudinal studies of regulatory trends—thereby facilitating extensive research on SEC oversight, corporate compliance, and market transparency. This study demonstrates the value of the process through descriptive statistics, methodological transparency, and its potential for integration with external financial datasets, underscoring its contribution to regulatory research and financial reporting quality. Data Availability: Data are available from the authors upon request. JEL Classifications: G14; G18; M41; M48.

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Publication Details

Journal
Journal of Information Systems
Published
2025-09-01
DOI
https://doi.org/10.2308/isys-2024-062
Primary Topic
Auditing, Earnings Management, Governance
Type
article
Field-Weighted Citation Impact
0.00
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article

Optimizing SEC Comment Letter Analysis with the CLARITY (Comment Letter Automated Retrieval and Intelligent Text Yielding) Process

Amira Bushra Ali Hassan, David Olsen
Journal of Information Systems
Auditing, Earnings Management, Governance
article

Optimizing SEC Comment Letter Analysis with the CLARITY (Comment Letter Automated Retrieval and Intelligent Text Yielding) Process

Amira Bushra Ali Hassan, David Olsen
article en

Abstract

ABSTRACT The CLARITY (Comment Letter Automated Retrieval and Intelligent Text Yielding) process offers a novel, automated approach for retrieving, cleaning, and analyzing U.S. Securities and Exchange Commission (SEC) comment letters. Addressing the challenges of volume, inconsistency, and accessibility, CLARITY retrieves letters from the EDGAR database spanning 2005–2024. Python-based tools ensure standardized data cleaning, robust error handling, and efficient processing. Unlike subscription-based platforms such as Audit Analytics (Audit Analytics 2024) or Intelligize (Intelligize 2024), CLARITY is open access, reproducible, and scalable. The resulting CLARITY dataset supports advanced analyses—such as sentiment analysis and longitudinal studies of regulatory trends—thereby facilitating extensive research on SEC oversight, corporate compliance, and market transparency. This study demonstrates the value of the process through descriptive statistics, methodological transparency, and its potential for integration with external financial datasets, underscoring its contribution to regulatory research and financial reporting quality. Data Availability: Data are available from the authors upon request. JEL Classifications: G14; G18; M41; M48.

Journal of Information Systems
Utah Tech University (US)
Openalex Percentile: Top 64%
Auditing, Earnings Management, Governance
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