Yawen Li , Yufei Xia , Huiyi Shi , Lingyun He , Yinguo Li
{"title":"Can bank regulatory technology alleviate financial mismatch? Causal evidence from double-debiased machine learning on bank-firm matched data","authors":"Yawen Li , Yufei Xia , Huiyi Shi , Lingyun He , Yinguo Li","doi":"10.1016/j.najef.2026.102604","DOIUrl":null,"url":null,"abstract":"<div><div>Financial mismatch (FM) remains a major challenge for firms, especially amid information asymmetry. The emergence of bank regulatory technology (RegTech) is reshaping regulation and risk management in banking. Utilizing a panel dataset of bank-firm matched loan-level data from 2014 to 2023, we employ double-debiased machine learning to provide empirical evidence that bank RegTech significantly reduces firms’ FM: one-standard-deviation increase in bank RegTech corresponds to at least a 2.29% reduction in the FM. This effect operates through three main channels: improved information transparency, eased financing constraints, and reduced managerial performance pressure. Investor attention amplifies the mitigating impact of bank RegTech on FM. The effects are heterogeneous, with more pronounced impacts observed among non-state-owned enterprises, high-tech firms, firms in less competitive industries, and firms with established bank-firm relationships. Results hold after rigorous robustness validation. Finally, we further demonstrate that reduced FM leads to lower operational risk and a decline in corporate financialization.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"83 ","pages":"Article 102604"},"PeriodicalIF":3.9000,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"North American Journal of Economics and Finance","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1062940826000264","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/2/20 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0
Abstract
Financial mismatch (FM) remains a major challenge for firms, especially amid information asymmetry. The emergence of bank regulatory technology (RegTech) is reshaping regulation and risk management in banking. Utilizing a panel dataset of bank-firm matched loan-level data from 2014 to 2023, we employ double-debiased machine learning to provide empirical evidence that bank RegTech significantly reduces firms’ FM: one-standard-deviation increase in bank RegTech corresponds to at least a 2.29% reduction in the FM. This effect operates through three main channels: improved information transparency, eased financing constraints, and reduced managerial performance pressure. Investor attention amplifies the mitigating impact of bank RegTech on FM. The effects are heterogeneous, with more pronounced impacts observed among non-state-owned enterprises, high-tech firms, firms in less competitive industries, and firms with established bank-firm relationships. Results hold after rigorous robustness validation. Finally, we further demonstrate that reduced FM leads to lower operational risk and a decline in corporate financialization.
期刊介绍:
The focus of the North-American Journal of Economics and Finance is on the economics of integration of goods, services, financial markets, at both regional and global levels with the role of economic policy in that process playing an important role. Both theoretical and empirical papers are welcome. Empirical and policy-related papers that rely on data and the experiences of countries outside North America are also welcome. Papers should offer concrete lessons about the ongoing process of globalization, or policy implications about how governments, domestic or international institutions, can improve the coordination of their activities. Empirical analysis should be capable of replication. Authors of accepted papers will be encouraged to supply data and computer programs.