Summary:
Against the backdrop of the current rapid development of the capital market, the quality of information disclosure is particularly critical to the quality development of companies. As a supplement to mandatory information disclosure, the interactive platforms of stock exchanges have become an important innovation in improving investor relations management through a two-way communication mechanism. On the interactive platform, instant Q&A between investors and companies can reflect not only the companies' response strategy to market dynamics, but also the level of voluntary disclosure by companies, which has become an important dimension in assessing the quality of information disclosure. Credit rating agencies (CRAs), as important information intermediaries in the capital market, rely on diversified information for their risk assessment. Therefore, the timeliness and unstructured feature of Q&A information on interactive platforms can be an effective reference for rating agencies. The rating system requires CRAs to combine quantitative information with qualitative analysis in an organic way, which further elevates the importance of non-standardized textual information in rating decisions. However, existing research focuses on the impact of financial indicators on rating outcomes, and the value of textual information disclosed via interactive platforms is still underexplored. Theoretically, the Q&A information of interactive platforms may significantly affect the level of corporate credit ratings through rating adjustment factors, but this path has not yet been empirically tested. Therefore, the purpose of this paper is to explore in depth the effect of interactive disclosure on corporate credit ratings and provide decision support for stakeholders. To address the above issues, we select a research sample of Chinese A-share listed companies in Shanghai and Shenzhen from 2013 to 2023. We find, first, that the negative tone of interactive disclosure texts and strategic managerial responses significantly lowers the level of issuer credit ratings, and this effect is more pronounced for rating agencies with higher reputations and foreign backgrounds. Second, the negative tone of interactive disclosure texts lowers issuer credit ratings by intensifying corporate financing constraints and increasing debt agency costs, and the strategic response lowers issuer credit ratings by increasing information asymmetry and decreasing stock liquidity. Third, the downgrade in issuer credit ratings caused by the negative tone of interactive disclosure texts and strategic responses may further reduce the amount of trade credit financing available to firms and increase the cost of bond issuance. The marginal contributions of this paper are mainly as follows: First, it enriches the research boundaries related to the economic consequences of interactive disclosure. Contrary to existing studies, the complex relationship between interactive disclosure and issuer credit ratings is explored in depth from the perspective of the textual features of interactive disclosure, further expanding the research boundaries on the economic consequences of interactive disclosure. Second, it enriches research on the factors influencing credit ratings. By using natural language processing (NLP) technology to construct textual indicators, the effective value of textual information of interactive disclosure on credit ratings is systematically presented. Third, from the perspective of debt financing, the economic impact of interactive disclosure on trade credit financing and bond issuance costs is examined through the financing intermediary role of credit ratings, further enriching research in this area. Fourth, by analyzing the mechanisms, heterogeneity and economic consequences of the textual features of interactive disclosure on issuer credit ratings, we not only deepen the theoretical understanding of the qualitative features of interactive disclosure and the determinants of credit ratings, but also provide useful policy insights for improving the construction of interactive platforms on stock exchanges to support better rating decisions by CRAs. The policy implications of this paper are as follows: from the corporate level, firms should establish a complete interactive information processing management system. First, companies can implement a categorical response mechanism, use NLP technology to identify high-frequency questions, and require the secretary of the board of directors to conduct a second-level verification and source annotation for important responses. Second, companies can develop an impact analysis system for information disclosure, and anticipate the impact of responses on issuer credit ratings and debt financing so as to avoid avoiding the truth. At the rating agency level, CARs should use sentiment analysis models to quantify the proportion of negative tone and capture changes in management attitudes. They can use semantic association mapping techniques to identify response strategies and incorporate the analysis into credit rating models. At the investor level, investors should explore the textual features of interactive information from multiple perspectives, quantify the proportion of both negative tone and the degree of strategic intent, and comprehensively assess the development prospects of companies. At the regulatory level, regulators should optimize the operating mechanism of the exchanges' interactive platform. First, regulators can establish a categorized review and quality assessment system that requires substantive responses and reduces ambiguity, with regular quality spot checks by the exchange. Second, regulators can jointly develop automated monitoring tools to capture and assess risk signals in real time and incorporate them into credit file management.
[1]敖小波、林晚发和李晓慧,2017,《内部控制质量与债券信用评级》,《审计研究》第2期,第57～64页。 [2]卞世博、陈曜、管之凡和周金花,2023,《高质量的互动可以提高股票价格信息效率吗——基于“上证e互动”的研究》,《会计研究》第4期,第102～117页。 [3]卞世博、陈曜和汪训孝,2022,《高质量的互动可以提高股票市场定价效率吗？——基于“上证e互动”的研究》,《经济学(季刊)》第3期,第749～772页。 [4]蔡贵龙、张亚楠、徐悦和卢锐,2022,《投资者—上市公司互动与资本市场资源配置效率——基于权益资本成本的经验证据》,《管理世界》第8期,第199～217页。 [5]曹新伟、洪剑峭和贾琬娇,2015,《分析师实地调研与资本市场信息效率——基于股价同步性的研究》,《经济管理》第8期,第141～150页。 [6]常莹莹和曾泉,2019,《环境信息透明度与企业信用评级——基于债券评级市场的经验证据》,《金融研究》第5期,第132～151页。 [7]陈关亭、连立帅和朱松,2021,《多重信用评级与债券融资成本——来自中国债券市场的经验证据》,《金融研究》第2期,第94～113页。 [8]丁慧、吕长江和陈运佳,2018,《投资者信息能力:意见分歧与股价崩盘风险——来自社交媒体“上证e互动”的证据》,《管理世界》第9期,第161～171页。 [9]管考磊、朱海宁和刘洋,2023,《网络平台互动能缓解资产误定价吗——来自交易所互动平台的经验证据》,《会计研究》第8期,第33～45页。 [10]李海燕和厉夫宁,2008,《独立审计对债权人的保护作用——来自债务代理成本的证据》,《审计研究》第3期,第81～93页。 [11]李文贵和路军,2022,《网络平台互动与股价崩盘风险:“沟通易”还是“操纵易”》,《中国工业经济》第7期,第178～196页。 [12]林晚发、方梅和沈宇航,2021,《债券募集说明书文本信息与债券发行定价》,《管理科学》第4期,第19～34页。 [13]林晚发、何剑波、周畅和张忠诚,2017,《“投资者付费”模式对“发行人付费”模式评级的影响:基于中债资信评级的实验证据》,《会计研究》第9期,第62～68页。 [14]林晚发、钟辉勇、赵仲匡和宋敏,2022,《金融中介机构竞争的市场反应——来自信用评级机构的证据》,《金融研究》第4期,第77～96页。 [15]刘娥平和施燕平,2014,《盈余管理、公司债券融资成本与首次信用评级》,《管理科学》第5期,第91～103页。 [16]刘欢、邓路和廖明情,2015,《公司的市场地位会影响商业信用规模吗？》,《系统工程理论与实践》第12期,第3119～3134页。 [17]刘星、张智慧和杨羚璇,2021,《分析师跟踪会影响企业主体信用评级吗？——基于我国信用债市场的经验证据》,《财贸研究》第1期,第69～82页。 [18]刘星和杨羚璇,2022,《信用评级变动能反映企业真实财务信息吗？——基于财务重述的视角》,《金融研究》第2期,第98～116页。 [19]孟庆斌、黄清华、张劲帆和王松,2020,《上市公司与投资者的互联网沟通具有信息含量吗？——基于深交所“互动易”的研究》,《经济学(季刊)》第2期,第637～662页。 [20]谭松涛、阚铄和崔小勇,2016,《互联网沟通能够改善市场信息效率吗？——基于深交所“互动易”网络平台的研究》,《金融研究》第3期,第174～188页。 [21]王雄元和张春强,2013,《声誉机制、信用评级与中期票据融资成本》,《金融研究》第8期,第150～164页。 [22]吴秋生和黄贤环,2017,《财务公司的职能配置与集团成员上市公司融资约束缓解》,《中国工业经济》第9期,第156～173页。 [23]吴育辉、翟玲玲、张润楠和魏志华,2020《“投资人付费”vs.“发行人付费”:谁的信用评级质量更高？》,《金融研究》第1期,第130～149页。 [24]熊家财和苏冬蔚,2016,《股票流动性与代理成本——基于随机前沿模型的实证研究》,《南开管理评论》第1期,第84～96页。 [25]徐寿福、郑迎飞和罗雨杰,2022,《网络平台互动与股票异质性风险》,《财经研究》第10期,第153～168页。 [26]徐寿福、郑迎飞和张嘉宸,2023,《网络平台互动、策略性回应与股票错误定价》,《经济管理》第11期,第189～208页。 [27]徐晓萍、阮永锋和刘音露,2018,《市场竞争降低评级质量了吗——基于新进入评级机构的实证研究》,《财贸经济》第11期,第96～111页。 [28]杨凡和张玉明,2020,《网络媒介、互动式信息披露与分析师行为——来自“上证e互动”的证据》,《山西财经大学学报》第11期,第113～126页。 [29]张春强、鲍群和盛明泉,2019,《公司债券违约的信用风险传染效应研究——来自同行业公司发债定价的经验证据》,《经济管理》第1期,第174～190页。 [30]赵杨、林琳和杨斌,2023,《互动式信息披露的质量特征及其影响因素研究》,《中国软科学》第7期,第127～141页。 [31]Attig, N., El Ghoul, S., Guedhami, O. and Suh, J., 2013, “Corporate Social Responsibility and Credit Ratings”, Journal of Business Ethics,117(4), pp. 679～694. [32]Bae, K., Kang, J. and Wang, J., 2015, “Does Increased Competition Affect Credit Ratings？ A Reexamination of the Effect of Fitch's Market Share on Credit Ratings in the Corporate Bond Market”, Journal of Financial and Quantitative Analysis, 50(5), pp. 1011～1035. [33]Baker, H. K., Dutta, S., Saadi, S. and Zhong, L., 2022, “Does Media Coverage Affect Credit Rating Change Decisions？”, Journal of Banking ＆ Finance, 145, p. 106667. [34]Brogaard, J., Li, D. and Xia, Y., 2017, “Stock Liquidity and Default Risk”, Journal of Financial Economics, 124(3), pp. 486～502. [35]Cornaggia, K. J., Krishnan, G. V. and Wang, C., 2017, “Managerial Ability and Credit Ratings”, Contemporary Accounting Research, 34(4), pp. 2094～2122. [36]Griffin, J. M. and Tang, D. Y., 2012, “Did Subjectivity Play a Role in CDO Credit Ratings？”, The Journal of Finance, 67(4), pp. 1293～1328. [37]Lee, C. M. C. and Zhong, Q., 2022, “Shall We Talk？ The Role of Interactive Investor Platforms in Corporate Communication”, Journal of Accounting and Economics, 74(2～3), p. 101524. [38]Li, C., Wu, M., Chen, X. and Huang, W., 2022, “Environmental, Social and Governance Performance, Corporate Transparency, and Credit Rating: Some Evidence from Chinese A-share Listed Companies”, Pacific～Basin Finance Journal, 74, p. 101806.
