Please wait a minute...
金融研究  2024, Vol. 531 Issue (9): 39-58    
  本期目录 | 过刊浏览 | 高级检索 |
银行数字化转型、信息结构与商业信用和银行信用间的替代性
卢垚, 战明华
广东外语外贸大学金融学院/金融开放发展研究院,
广州华南财富管理中心研究基地,广东广州 510006
The Digital Transformation of Banks, Information Structure, and the Substitutability between Trade Credit and Bank Credit
LU Yao, ZHAN Minghua
School of Finance/Institute of Financial Openness and Development, Guangdong University of Foreign Studies;
Institute of Fortune Management Research
下载:  PDF (1350KB) 
输出:  BibTeX | EndNote (RIS)      
摘要 信息不对称是造成企业融资约束的重要原因,那么银行数字化转型是否有利于缓解银企间信息不对称问题,促进银行信用替代商业信用呢?本文创新性地将信贷市场信息划分为可数字化和不可数字化两种信息类型,并提出银行数字化转型有助于银行处理数字化信息,但弱化了银行处理非数字化信息的能力,进而对两类信用的替代关系产生非线性影响。本文首先围绕银行数字化转型如何影响企业对银行信用和商业信用两类信用选择的问题展开理论分析,然后采用中国非金融上市公司银企贷款和供应链数据对假说进行实证检验。研究发现:一是银行数字化转型总体上促进了银行信用对商业信用的替代;二是银行信用对商业信用的替代是非线性的,原因在于数字化转型使银行处理非数字化和数字化两类信息的能力此消彼长;三是银行数字化转型对银行信用替代商业信用的影响程度与上下游企业的相对规模、经济周期阶段及企业所在行业类型相关。本研究对于如何在金融科技迅速发展背景下提高金融资源配置效率,促进经济高质量发展,均具有重要政策含义。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
卢垚
战明华
关键词:  银行数字化转型  信息结构差异  商业信用  银行信用  函数系数回归    
Summary:  Due to the broader market scope of bank credit, from both the perspective of Arrow-Debreu economic Pareto efficiency and the financial accelerator effect, the substitution of bank credit for trade credit plays a significant role in promoting high-quality development at both the micro and macro levels. In recent years, the rapid development of digital technology has fundamentally transformed China's financial structure and operations. Existing research generally holds that fintech can effectively reduce information asymmetry, thereby mitigating financial friction. Theoretically, this should increase firms' reliance on bank credit and reduce trade credit usage. However, this view overlooks the heterogeneity of information in the bank-enterprise credit market: some information, such as credit scores, is digitizable, while other aspects, like leader ship quality, are not digitizable. While digital transformation enhances the ability to process digitizable information, it fails to address non-digitizable information asymmetry. Additionally, as digitalization progresses, banks are reducing front-line operations that handle non-digitizable information. As a result, the impact of digital transformation on the relationship between banks and trade credit becomes uncertain.
To explore this issue, we categorize credit market information into digitizable and non-digitizable types and examine how bank digital transformation influences the substitutability between trade credit and bank credit. Using an extended bank-enterprise relationship framework, this paper analyzes how digital transformation alters credit market friction and affects firms' credit choices. Based on the model analysis, we propose two hypotheses and test them using data from non-financial listed companies in China (2010-2021) with a functional-coefficient regression model. The results show that the effect of bank digital transformation on credit substitutability is nonlinear. Its impact increases as the proportion of digitizable information grows, with stronger substitution observed when downstream firms are larger and have more digitizable information. This suggests that, although bank digitalization has opposing effects on agency costs, the cost-reducing is dominant. Further analysis reveals that bank digital transformation affects credit substitutability by influencing the marginal cost of bank credit through information processing. The substitution relationship is also sensitive to economic cycles and industry types, being more pronounced during economic expansions. Significant industry differences are noticeable, with high-tech industries relying more on digitizable information, while manufacturing industries depend more on non-digitizable information, making digitalization's impact weaker in the latter.
This paper makes three potential contributions. First, it classifies bank-enterprise credit market information into digitizable and non-digitizable types, proposing a theoretical framework that reveals the dual role of bank digitalization in addressing information asymmetry caused by heterogeneous information in credit markets. Second, based on the characteristics and acquisition paths of digitizable and non-digitizable information, this paper develops an information structure index, demonstrating that bank digital transformation, rather than other mechanisms, affects the substitutability between trade credit and bank credit. Third, consistent with the theoretical model's logic, we employ a functional-coefficient regression model to empirically test the nonlinear relationship, revealing that the impact of bank digital transformation on firms' credit choices is nonlinearly related to the information structure, and confirming that the key mechanism through which digital transformation affects the relationship between trade credit and bank credit is the marginal cost of firms choosing bank credit.
In the context of rapid fintech development, this study has important policy implications for optimizing financial regulation and macroeconomic management. First, as banks' digital transformation strengthens the substitution of bank credit for commercial credit, policymakers can further support digitalization while maintaining traditional credit services to ensure more efficient resource allocation. Second, although enhanced bank credit substitution improves financial stability and mitigates risks within the banking system, it may also introduce new risks, requiring innovative regulatory approaches. Finally, considering that enterprises with different information advantages have different risk premiums when obtaining bank credit, policymakers should innovate structural monetary policy tools to encourage banks to provide differentiated credit products according to the information structure of different enterprises.
Keywords:  Bank Digital Transformation    Heterogeneous Information Structure    Trade Credit    Bank Credit    Function Coefficient Regression
JEL分类号:  G21   G23   C51  
基金资助: * 本文感谢国家自然科学基金青年科学基金项目(72403057)和广东省哲学社会科学规划项目(GD24CLJ02)的资助。感谢匿名审稿人的宝贵意见,文责自负。
通讯作者:  战明华,管理学博士,教授,广东外语外贸大学金融学院/金融开放发展研究院,E-mail:zhanmheco@163.com.   
作者简介:  卢垚,经济学博士,讲师,广东外语外贸大学金融学院,广州华南财富管理中心研究基地,E-mail:luyao300@foxmail.com.
引用本文:    
卢垚, 战明华. 银行数字化转型、信息结构与商业信用和银行信用间的替代性[J]. 金融研究, 2024, 531(9): 39-58.
LU Yao, ZHAN Minghua. The Digital Transformation of Banks, Information Structure, and the Substitutability between Trade Credit and Bank Credit. Journal of Financial Research, 2024, 531(9): 39-58.
链接本文:  
http://www.jryj.org.cn/CN/  或          http://www.jryj.org.cn/CN/Y2024/V531/I9/39
[1] 陈登科和陈诗一,2018,《资本劳动相对价格、替代弹性与劳动收入份额》,《世界经济》第12期,第73~97页。
[2] 陈胜蓝和刘晓玲,2018,《经济政策不确定性与公司商业信用供给》,《金融研究》第5期,第172~190页。
[3] 陈胜蓝和马慧,2018,《贷款可获得性与公司商业信用》,《管理世界》第11期,第108~120+149页。
[4] 侯世英和宋良荣,2019,《金融科技背景下中小银行转型研究:背景、战略布局与建议》,《当代经济管理》第5期,第85~91页。
[5] 胡悦和吴文锋,2022,《商业信用融资和我国企业债务的结构性问题》,《经济学(季刊)》第1期,第257~280页。
[6] 黄兴孪、邓路和曲悠,2016,《货币政策、商业信用与公司投资行为》,《会计研究》第2期,第58~65页。
[7] 孔东民、李海洋和杨薇,2021,《定向降准、贷款可得性与小微企业商业信用》,《金融研究》第3期,第77~94页。
[8] 刘艳霞、王芝皓和田茂再,2024,《变系数部分函数型线性模型的复合分位数回归估计》,《数理统计与管理》第1期,第1~12页。
[9] 罗煜、崔书言和旷纯,2022,《数字化与商业银行经营转型——基于传统业务结构变迁视角》,《国际金融研究》第5期,第34~44页。
[10] 饶品贵和姜国华,2013,《货币政策对银行信贷与商业信用互动关系影响研究》,《经济研究》第1期,第69~82+150页。
[11] 孙浦阳、李飞跃和顾凌骏,2014,《商业信用能否成为企业有效的融资渠道》,《经济学(季刊)》第4期,第1647~1652页。
[12] 王彦超,2014,《金融抑制与商业信用二次配置功能》,《经济研究》第6期,第86~99页。
[13] 谢绚丽和王诗卉,2022,《中国商业银行数字化转型:测度、进程及影响》,《经济学(季刊)》第6期,第1937~1956页。
[14] 张杰、刘元春、翟福昕和芦哲,2013,《银行歧视、商业信用与企业发展》,《世界经济》第9期,第94~126页。
[15] 钟凯、梁鹏、董晓丹和王秀丽,2022,《数字普惠金融与商业信用二次配置》,《中国工业经济》第1期,第170~188页。
[16] 战明华和卢垚,2023,《中国货币政策存在跨周期调控吗》,《财贸经济》第5期,第91~107页。
[17] Angori, G., D. Aristei and M. Gallo, 2020, “Banking Relationships, Firm-size Heterogeneity and Access to Credit: Evidence from European Firms”, Finance Research Letters, 33, 101231.
[18] Aoki, M., 2001,“Toward a Comparative Institutional Analysis”, Economic Systems, 26(4), pp.412 ~414.
[19] Beck, T., H. Degryse, R. De Haas, and N.Horen, 2018. “When Arm's Length is too Far: Relationship Banking over the Credit Cycle”, Journal of Financial Economics. 127(1), pp.174~196.
[20] Berger, A.N.and G.F. Udell, 2006, “A More Complete Conceptual Framework for SME Finance”, Journal of Banking and Finance, 30(11), pp.2945~2966.
[21] Cenni, S., S. Monferrà, V. Salotti, M.Sangiorgi and G.Torluccio, 2015, “Credit Rationing and Relationship Lending. Does Firm Size Matter?”, Journal of Banking and Finance, 53(4), pp.249~265.
[22] Emery, G.W., 1987, “An Optimal Financial Response to Variable Demand”, Journal of Financial and Quantitattive Analysis, 22(2), pp. 909~225.
[23] Guerrieri, L. and M. Iacoviello, 2017, “Collateral Constraints and Macroeconomic Asymmetries”, Journal of Monetary Economics, 90(10), pp. 28~49.
[24] Maria, P. and E. Konstantinos, 2015, “Trade Credit, Bank Credit, and Flight to Quality: Evidence from French SMEs”,Journal of Small Business Management, 53(4), pp. 1219~1240.
[25] Mian, S.and C.W. Smith, 1992, “Accounts Receivable Management Policy: Theory and Evidence”, Journal of Finance, 47(1), pp.169~200.
[26] Pasqualina, A., A. Gianfranco and D. Luca, 2023, “The Signaling Role of Trade Credit: Evidence from a Counterfactual Analysis”, Journal of Corporate Finance, 80(6), 102414.
[27] Petersen, M. A.and R.G. Rajan, 1997, “Trade Credit: Theories and Evidence”, Review of Financial Studies, 10(3), pp.661~691.
[1] 华秀萍, 程思睿, 李婉宁, 王勇. 非正式融资中的文化力量 ——企业文化对商业信用的影响[J]. 金融研究, 2023, 520(10): 186-206.
[2] 吕怀立, 王文明, 鄢姿俏, 侯亮. 金融政策竞争中性与民营企业融资纾困——来自突发公共卫生事件的准自然实验[J]. 金融研究, 2021, 493(7): 95-114.
[3] 孔东民, 李海洋, 杨薇. 定向降准、贷款可得性与小微企业商业信用——基于断点回归的经验证据[J]. 金融研究, 2021, 489(3): 77-94.
[4] 陈胜蓝, 刘晓玲. 中国城际高铁与商业信用供给——基于准自然实验的研究[J]. 金融研究, 2019, 472(10): 117-134.
[5] 陈胜蓝, 刘晓玲. 经济政策不确定性与公司商业信用供给[J]. 金融研究, 2018, 455(5): 172-190.
[6] 姜军, 申丹琳, 江轩宇, 伊志宏. 债权人保护与企业创新[J]. 金融研究, 2017, 449(11): 128-142.
[1] 肖欣荣, 周晏伊. 中国A股涨跌停交易制度与投资者处置效应[J]. 金融研究, 2024, 531(9): 153 -170 .
[2] 叶帅, 张劲帆, 郑凯轩. 中国新基金过度发行之谜和投资者保护[J]. 金融研究, 2024, 531(9): 171 -188 .
[3] 王熙, 黄德金, 高明. 波动率指数与价格发现——基于中国市场的理论拓展[J]. 金融研究, 2024, 530(8): 113 -131 .
[4] 宋芳秀, 宋奎壁. 公众异质预期、信息成本与货币政策传导[J]. 金融研究, 2024, 529(7): 1 -19 .
[5] 沈艳, 江弘毅, 胡诗云, 赵家琪, 黄卓. 数字金融支持高质量发展:理论、机制和证据[J]. 金融研究, 2024, 529(7): 20 -39 .
[6] 栾稀, 朱浣君, 彭雨洁, 徐奇渊. 地缘政治冲击下的主权债务风险:溢出效应和传导渠道[J]. 金融研究, 2024, 530(8): 1 -19 .
[7] 陈学彬, 李鑫. 关联网络视角下的国际外汇市场汇率风险溢出研究[J]. 金融研究, 2024, 530(8): 20 -38 .
[8] 李政, 李薇, 李丽雯. 美国三类不确定性冲击、生产网络传导与中国行业尾部风险[J]. 金融研究, 2024, 530(8): 39 -57 .
[9] 欧阳远芬, 王秋实. 基于隐性担保和显性担保比较的城投债定价研究[J]. 金融研究, 2024, 530(8): 77 -94 .
[10] 杨娇辉, 王伟, 冯云. 经济政策不确定性、国家宏观风险与国际证券投资[J]. 金融研究, 2024, 530(8): 58 -76 .
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《金融研究》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn
京ICP备11029882号-1