Can market information outperform hard and soft information in predicting corporate defaults?

IF 2.8 3区 经济学 Q2 BUSINESS, FINANCE
Stefano Filomeni, Udichibarna Bose, Anastasios Megaritis, Athanasios Triantafyllou
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Abstract

Recent evidence has shown that hybrid models for credit ratings are important when assessing the risk of firms. Within this stream of literature, we aim to provide novel evidence on how hard (quantitative), soft (qualitative), and market information predict corporate defaults for unlisted firms by implementing the Cox proportional hazard model. We address this research question by exploiting a unique proprietary dataset comprising of detailed information on internal credit ratings of European unlisted mid-sized firms and compute their Merton's distance-to-default (DD) measure of credit risk with market data collected on comparable publicly listed companies. Our results show that the bank's use of hard, soft, and market information when assessing the credit ratings of borrowers has a significant influence on the prediction of their defaults. Further, we investigate the significant influence of soft information in predicting corporate defaults by drawing on two separate processes through which loan officers can inject soft information in credit scoring, that is, ‘codified’ and ‘uncodified’ discretion. Finally, when we distinguish between the loan officer's discretion to upgrade or downgrade an applicant's credit score, we find that it is the upgrade that is likely to predict a lower probability of a firm defaulting. This study contributes to the policy debate on safeguarding the banking sector's continuity by positing that integrating market information into banks' hybrid methods of credit rating helps to improve the accuracy in predicting unlisted firms' credit risk that is useful to policy makers for the design of future forward-looking financial risk management frameworks.

Abstract Image

在预测公司违约方面,市场信息能否胜过硬信息和软信息?
最近的证据表明,信用评级的混合模型在评估企业风险时非常重要。在这一文献流中,我们旨在通过实施考克斯比例危险模型,提供有关硬信息(定量)、软信息(定性)和市场信息如何预测非上市公司违约的新证据。为了解决这一研究问题,我们利用了一个独特的专有数据集,其中包括欧洲非上市中型企业内部信用评级的详细信息,并利用收集到的可比上市公司的市场数据计算了它们的默顿违约距离(DD)信用风险度量。我们的研究结果表明,银行在评估借款人信用等级时对硬信息、软信息和市场信息的使用对借款人违约预测有显著影响。此外,我们通过贷款人员在信用评分中注入软信息的两个不同过程,即 "编纂 "和 "非编纂 "自由裁量权,研究了软信息在预测企业违约方面的重要影响。最后,当我们区分贷款官员提升或降低申请人信用评分的自由裁量权时,我们发现提升信用评分可能会降低企业违约的概率。本研究认为,将市场信息纳入银行的混合信用评级方法有助于提高非上市公司信用风险预测的准确性,这对政策制定者设计未来的前瞻性金融风险管理框架很有帮助,从而为保障银行业连续性的政策辩论做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.70
自引率
6.90%
发文量
143
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