纳税信用评级结果披露与研发投入

以纳税信用评级披露作为一个天然外生冲击,并基于2013—2016年1214家A股上市公司的微观数据构造准自然实验,使用双重差分法系统评估纳税信用评级结果披露对上市公司研发投入的影响。结果发现:纳税信用评级结果披露显著增加了上市公司的研发投入。基于PSM-DID方法的估计结果与上述结论无明显差异。稳健性检验也表明上述结论的正确性。机制检验表明,纳税信用评级结果披露通过降低企业的融资约束,进而促进企业增加研发投入。此外,分样本回归发现纳税信用评级结果披露只能对中小型企业和民营企业的研发投入产生促进作用。     

Remittances and rural credit markets: Evidence from Senegal

This study investigates the relationship between remittances and credit markets in Senegal, focusing on rural areas where financial constraints are more challenging. Using a household fixed effects model, the findings show that remittances and credit markets are complements; namely, the receipt of remittances is positively associated with the likelihood of having a loan in a household. This means that migrants can increase the reliability of their family members and close relatives back home through their remittances, insuring them vis‐à‐vis lenders for their credit contracts. They are the collateral or the “element of trust” in the credit contract between the borrower and the lender, representing a potential alternative in case of non‐repayment. This result is robust to alternative models and various robustness tests mitigating the potential endogeneity of remittances. A detailed analysis also shows that the relationship between remittances and credit markets is mainly driven by loans taken for consumption and food, in particular, as well as loans provided by informal institutions.     

Tax evasion by tax deferment: Sham litigation with an informal credit market

We show theoretically how tax evasion is facilitated by informal credit market through tax deferment. Our model is empirically based. Using sham litigation, tax evaders earn a higher rate of return than the stipulated penalty rate for tax evasion while the government loses tax revenue. We propose an upfront part–payment of the disputed amount of tax as a solution to the form of tax evasion we describe.     

Do significant risk warnings in annual reports increase corporate bond credit spreads? Evidence from China

Based on listed companies issuing bonds on the Shanghai and Shenzhen Stock Exchanges from 2007 to 2017, this study analyzes the relationship between significant risk warnings in Chinese companies’ annual reports and corporate bond credit spreads. The main findings are as follows. First, in the Chinese market, “substantial warnings of significant risks” can significantly improve corporate bond credit spreads, reflecting the risk-warning effect; second, state-owned property rights weaken this effect, which only pertains to listed companies with poor risk management and low information quality; third, significant risk warnings increase investors’ heterogeneous beliefs, also affecting credit spreads; and fourth, through textual analysis, it is found that the corporate bond credit spread is greater when the disclosed risk factors are more pessimistic and less similar to those of the previous year. The findings of this paper help to enrich the literature on credit spreads and risk disclosure.     

Does Targeted Monetary Policy Matter? A Perspective from China

Several major central banks have experimented with targeted monetary policy to improve credit resource allocation. This policy only applies to ‘eligible’ banks. For example, The People's Bank of China conducted seven targeted reductions of reserve requirements during 2014–15. This article documents the phenomenon of targeted monetary policy and evaluates its effects. The results show that, in the case of China, this policy has generated an extra significant, positive return on the stocks of eligible banks, amounting to 1.2–1.3 per cent in a four‐day treatment period. This substantial return gives commercial banks an extra incentive to align with the policy goals of central banks.     

Assessing the Reliability of Self‐reported Income Information in Informal Small Business Lending through a Bogus Pipeline Experiment

Coping with asymmetric information plays a major role in successful small business lending. Our purpose is to determine if small business applicants report their income information correctly when requesting a loan. We use a randomised controlled trial bogus pipeline experiment, established during a typical cash‐flow analysis of a bank for small businesses in the Philippines. The bogus pipeline approach is commonly applied in social science and aims to increase the rate of truth telling by informing participants that answers will be verified by a lie detector. The experimental data, which include 243 observations of credit clients that are mainly from the agricultural and food value chain, served to identify asymmetric information. Additionally, debtors’ repayment behaviour for approved loans was observed by the bank. Our results indicate that loan applicants of the treatment group report lower incomes, an effect which is most pronounced in lower income quantile. Our analyses also reveal higher loan delinquencies in the control group.     

基于DEA模型的建筑业环境行为效率评价研究

对建筑企业进行环境行为效率评价是促进我国建筑行业健康可持续发展的重要手段。根据建筑行业特点及我国现有的企业环境信用标准,构建了建筑企业环境行为评价的投入产出指标体系,将改进的数据包络分析方法模型运用到企业环境行为效率评价领域,解决了现有企业评价研究主观性突出的问题,得出建筑企业评价分析方法。在此基础上对某代表性建筑企业2010—2017年环境行为数据进行了评价分析。分析结果表明该企业环境行为总体效率良好,但鉴于出现了奇数年和偶数年的效率波动情况,仍需提高环境行为效率。     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

客户特征对商业银行信用卡业务盈利水平的影响

以中国商业银行的92837个信用卡客户为研究样本,根据客户的自然特征,运用两步聚类法对之进行分类,甄别出高盈利客户的特征。利用客户样本过去一年的交易记录测算各类客户对应的信用卡业务的盈利水平。以能给银行信用卡业务带来高盈利的客户为例,对其特征(包括自然特征和消费行为特征)与银行信用卡业务盈利水平的关系进行回归分析。研究结果显示:信用卡客户的自然特征和消费行为特征与商业银行信用卡业务的盈利水平具有一定相关性。提出:商业银行应运用数据挖掘技术精准识别高盈利客群、科学预估客户的盈利水平、合理配置营销资源以实现客户终身价值最大化。     

A hybrid information approach to predict corporate credit risk

This study proposes a hybrid information approach to predict corporate credit risk. In contrast to the previous literature that debates which credit risk model is the best, we pool information from a diverse set of structural and reduced‐form models to produce a model combination based on credit risk prediction. Compared with each single model, the pooled strategies yield consistently lower average risk prediction errors over time. We also find that while the reduced‐form models contribute more in the pooled strategies for speculative‐grade names and longer maturities, the structural models have higher weights for shorter maturities and investment grade names.