Predictive Power of Auditors’ industry-specific knowledge on Macroeconomic Growth: An Empirical Estimation Utilizing Machine Learning

Predictive Power of Auditors’ industry-specific knowledge on Macroeconomic Growth: An Empirical Estimation Utilizing Machine Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predictive Power of Auditors’ industry-specific knowledge on Macroeconomic Growth: An Empirical Estimation Utilizing Machine Learning LITAN WANG, Ziyu Wang, Yongzhe Wang, Chunyi Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8283132/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Auditors’industry-specific knowledge is a critical production factor whose broader macroeconomic implications remain underexplored. Drawing on the co-production theory (or synergistic production theory), this study empirically investigates the link between Auditors’ industry-specific knowledge and economic growth using a comprehensive panel dataset of Chinese listed companies from 2009 to 2023. We employ a Generalized Random Forest (GRF) model — a machine learning methodology—to accurately predict and identify the non-linear relationship between these two variables. The findings reveal that auditors’industry-specific knowledge significantly promotes economic growth through two distinct pathways: (1) enhancing the efficacy of local audit firms and (2) optimizing the corporate governance mechanisms of the Audit Committee. This positive effect is particularly pronounced in the power and energy sector and in the Western regions of China, peaking notably in 2011. This research makes several innovative contributions. Firstly, it expands the theoretical framework for analyzing the macroeconomic effects of audit services. Secondly, it pioneers the application of machine learning methods (GRF) to precisely identify the complex, non-linear relationship in this context. Thirdly, it uncovers significant regional and sectoral heterogeneity in the impact of industry expertise. Based on these results, we recommend three policy actions: (1) strategically supporting the development of industry specialization within local audit firms; (2) enhancing the governance framework of audit committees; and (3) bolstering professional audit support during periods of economic downturn. This study offers crucial reference material for policymakers aiming to cultivate high-quality audit services and an independent knowledge infrastructure. Business and commerce/Business and management Social science/Business and management Business and commerce/Economics Social science/Economics Business and commerce/Finance Social science/Finance Business and commerce/Information systems and information technology Auditors’ Industry-specific Knowledge Macroeconomic Growth Aggregate Accounting Earnings Generalized Random Forest (GRF) Machine Learning Full Text Additional Declarations No competing interests reported. Supplementary Files Highlights.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8283132","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":575618729,"identity":"410dc1e3-84d5-4933-a177-6fa24e9e0cae","order_by":0,"name":"LITAN WANG","email":"","orcid":"","institution":"Shanghai Lixin University of Accounting and Finance","correspondingAuthor":false,"prefix":"","firstName":"LITAN","middleName":"","lastName":"WANG","suffix":""},{"id":575618732,"identity":"38598600-7e5e-4c56-8815-0a4062ddf130","order_by":1,"name":"Ziyu Wang","email":"","orcid":"","institution":"Shanghai Lixin University of Accounting and 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Drawing on the co-production theory (or synergistic production theory), this study empirically investigates the link between Auditors’ industry-specific knowledge and economic growth using a comprehensive panel dataset of Chinese listed companies from 2009 to 2023. We employ a Generalized Random Forest (GRF) model — a machine learning methodology—to accurately predict and identify the non-linear relationship between these two variables.\u003c/p\u003e\n\u003cp\u003eThe findings reveal that auditors’industry-specific knowledge significantly promotes economic growth through two distinct pathways: (1) enhancing the efficacy of local audit firms and (2) optimizing the corporate governance mechanisms of the Audit Committee. This positive effect is particularly pronounced in the power and energy sector and in the Western regions of China, peaking notably in 2011.\u003c/p\u003e\n\u003cp\u003eThis research makes several innovative contributions. 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