Investments,Credit, and Corporate Financial Distress: Evidence from Central and Eastern Europe

Although they are instrumental for economic development, productivity-enhancing corporate investments may increase the financial vulnerability of companies, especially in an economic and financial crisis. We employ an instrumental probit model with the aim of finding evidence for the investment and credit patterns that led companies into financial distress during the global financial crisis 2009–2010. The company-level micro-data for our study on three Central and East European countries—Hungary, Bulgaria, Romania and two Baltic countries, Latvia and Lithuania—originates from two independent surveys, the Business Environment and Enterprise Performance Survey conducted in 2008 and the Financial Crisis Survey conducted in 2009/2010. Both were carried out jointly by the EBRD and the World Bank. Our results emphasize a substantial adverse impact from investment intensity and debt financing on company financial soundness during a crisis. On top of that, we discover a strong non-linear pattern in the sensitivity of company distress to its investment-financing nexus.     

Credit Risk Research: Review and Agenda

This article provides a comprehensive review of scholarly research on credit risk measurement during the last 57 years applying bibliometric citation analysis and elaborates an agenda for future research. The bibliography is compiled using the Institute for Scientific Information (ISI) Web of Science (WOS) database and includes all articles with citations over the period 1960–2016. Specifically, the review is carried out using 1695 articles across 72 countries published in 442 journals by 2928 authors. The findings suggest that credit risk research is multifaceted and can be classified into six streams: (1) defaultable security pricing, (2) default intensity modeling, (3) comparative analysis of credit models, (4) comparative analysis of credit markets, (5) credit default swap (CDS) pricing, and (6) loan loss provisions. The article contributes through synthesizing and identifying existing as well as emerging research streams.     

信贷供给、资产配置及企业绩效——来自中国制造业上市公司的证据

近些年,中国“金融与实体经济”的研究愈发成为热点。宏观层面,资金“脱实入虚”“金融空转”的讨论亦此起彼伏。然而,针对微观层面的企业资金“脱实入虚”的研究则寥寥无几。本文选取2003—2016年中国A股制造业上市公司的年度报告数据,利用2009年宏观调控措施营造的“准自然实验”环境,采用双重差分方法探究在资金资源充足的前提下,企业配置金融资产的倾向性,并进一步研究企业绩效的后续变化情况。实证结果表明,当制造业企业获得更多的信贷资金支持,同时缺乏有效的外部监管时,企业更倾向于配置金融资产,金融活动也会在企业的日常生产经营中扮演愈发重要的角色。在此基础上,企业的主营业务却未得到发展,反而盈利能力、成长能力均受到损害,且后者受到的影响更大,即进行过多金融投资对中国A股制造业上市公司的远期影响更为深重。基于上述结论,本文从实体经济从业者、资金提供方以及监管层三个角度提出了相关政策建议。     

言用的跨学科概念平台—信用的基本问题研究系列之一

各学科的信用研究一直没能实现很好的进展。一个重要根源是，传统的信用解释不能为各学科的信用研究提供一个统一的概念平台，以促成各个学科的研究形成合力。通过对信用本身特性的分析可以发现，信用的本质性规定可重新界定为“信用即信任之效用”。这一新界定能够使多学科的信用概念找到一个统一的基础，能够消解传统信用概念使用中的矛盾，能够很好地理顺信用与其他相关概念的关系，从而可以成为多学科信用研究的概念平台。     

“暴力征地”现象的产权经济学解释

文章认为国内出现的大量的"暴力征地"现象是由两个原因造成的:一个是中国土地产权的残缺,尤其是土地使用权和转让权的限制,另一个是交易费用,包括量度费用和信息费用,在此基础上推出的两个含义都得到了事实的验证。     

Deciphering big data in consumer credit evaluation

This paper examines the impact of large-scale alternative data on predicting consumer delinquency. Using a proprietary double-blinded test from a traditional lender, we find that the big data credit score predicts an individual’s likelihood of defaulting on a loan with 18.4% greater accuracy than the lender’s internal score. Moreover, the impact of the big data credit score is more significant when evaluating borrowers without public credit records. We also provide evidence that big data have the potential to correct financial misreporting.     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

The Bank Lending Channel Revisited

A central proposition in research on the role of banks in the transmission mechanism is that monetary policy imparts a direct impact on deposits and that deposits act as the driving force of bank lending. This paper argues that the emphasis on policy‐induced changes in deposits is misplaced. A reformulation of the bank lending channel is proposed that works primarily through the impact of monetary policy on banks’ balance sheet strength and risk perception. Such a recasting implies, contrary to conventional wisdom, that greater reliance on market‐based funding enhances the importance of the channel.     

The productivity of agricultural credit in India

This study examines the nature of the relationship between formal agricultural credit and agricultural Gross Domestic Product (GDP) in India, specifically the role of the former in supporting agricultural growth, using state level panel data covering the period 1995–1996 to 2011–2012. The study uses a mediation analysis framework to map the pathways through which institutional credit relates to agricultural GDP relying on a control function approach to tackle the problem of endogeneity. The findings from the analysis suggest that over this period, all the inputs are highly responsive to an increase in institutional credit to agriculture. A 10% increase in credit flow in nominal terms leads to an increase by 1.7% in fertilizers (N, P, K) consumption in physical quantities, 5.1% increase in the tonnes of pesticides, 10.8% increase in tractor purchases. Overall, it seems quite clear that input use is sensitive to credit flow, whereas GDP of agriculture is not. Credit seems therefore to be an enabling input, but one whose effectiveness is undermined by low technical efficiency and productivity.