银行业联合授信制度制约企业的非效率投资了吗*——基于沪深A股公司的分析

中国银保监会出台联合授信管理制度旨在抑制企业过度融资行为，提高资金使用效率。为检验该制度是否达到预期效果，采用2014—2018年沪深A股上市公司样本，运用非平衡面板数据回归实证考察联合授信机制对企业非效率投资的影响，发现联合授信对企业的非效率投资（包含投资不足和投资过度）呈现显著抑制效应，其中联合授信对投资过度的抑制效应最大。进一步研究发现，相较于国有、大规模、低负债、高盈利等异质属性的企业，联合授信对非国有、小规模、高负债、低盈利等属性企业的非效率投资（尤其是投资过度）抑制效应更为显著。     

Situating the Asian Infrastructure Investment Bank in the context of global economic governance

The creation of the Asian Infrastructure Investment Bank (AIIB)was welcomed by the World Bank but opposed by the Obamaadministration. The paper explains China’s positive relationshipwith the Bank and the Organization for Economic Cooperation andDevelopment (OECD) in terms of the mission of the Bank, sharedby the OECD, to develop and deepen the global economy. The AIIBand the related Belt and Road initiative promise to do this throughinvestment in infrastructure and connectivity in and around thepoorly integrated Eurasian landmass. But while the current Chineseleadership has supported an inclusive global economy based uponfree trade and supported by multilateral institutions, China’s controlof resources outside the multilateral framework and adherence topractices that challenge liberal principles prompt suspicions thatthese commitments are either disingenuous or anyway subjectto reversal. In itself, therefore, the AIIB provides no conclusiveevidence either way on China’s future course.     

Best classification algorithms in peer-to-peer lending

A proper credit scoring technique is vital to the long-term success of all kinds of financial institutions, including peer-to-peer (P2P) lending platforms. The main contribution of our paper is the robust ranking of 10 different classification techniques based on a real-world P2P lending data set. Our data set comes from the Lending Club covering the 2009–2013 period, which contains 212,252 records and 23 different variables. Unlike other researchers, we use a data sample which contains the final loan resolution for all loans. We built our research using a 5-fold cross-validation method and 6 different classification performance measurements. Our results show that logistic regression, artificial neural networks, and linear discriminant analysis are the three best algorithms based on the Lending Club data. Conversely, we identify k-nearest neighbors and classification and regression tree as the two worst classification methods.     

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.     

Clearinghouse loan certificates as a lender of last resort

Looking across multiple panics of the nineteenth and twentieth centuries, this paper treats borrowing of clearinghouse loan certificates as borrowing from a lender of last resort. We evaluate individual bank use of clearinghouse loan certificates in New York City using bank balance sheet data. Bank capital ratios do not predict positive net borrowing. Lower pre-panic reserve ratios increased the probability of positive net borrowing of loan certificates. Bank borrowing behavior from a lender of last resort remained relatively constant across all three crises considered.     

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。     

Independence in bank governance structure: Empirical evidence of effects on bank risk and performance

We investigate how different governance arrangements affect risk and return in banks. Using a new data set for UK banks over the period 2003–2012, we employ a simultaneous equations framework to control for the reciprocal relationship between risk and return. We show that separation of the roles of CEO and Chairman increases bank risk without causing a concurrent increase in return. We also find that oversight by a Remuneration Committee and Non-Executive Directors (NEDs) lowers the probability of bank failure, indicating that empowering an independent Chairman has different effects from empowering independent NEDs. Overall, our results underline the importance of accounting for the heterogeneity in corporate governance arrangements within banks.     

Bank business models and liquidity creation

Using a large panel of US bank holding companies from 2001 to 2015, this study investigates the association between functional diversification and bank liquidity creation. I document evidence of lower liquidity creation for higher diversification. The effect of moving into nontraditional activities on liquidity creation is more apparent with large banks and less pronounced with small banks. The impact of diversification on liquidity creation is less significant during the late stage of crisis and is more clearly observed in small and medium-sized banks. Low liquidity creation banks, leveraged by a higher share of non-interest income, are more likely to further decrease their liquidity creation. The study is of interest to regulators and policymakers who are concerned about bank business models.     

Market response to syndicated loan announcements from high-profile failed and acquiring banks during the global financial crisis

Previous studies that have examined the impact of the 2008 financial crisis on syndicated loans have ignored potential differences between lending banks by explicitly or implicitly aggregating all lenders together and focusing on borrower characteristics. One must jointly consider both borrower and lender to fully understand the complex role of the syndicate during this period. We consider the identity of the lender, with a focus on five major US banks that failed and their five corresponding acquirers. Our results highlight the distinct roles of investment and commercial banks and facilitate an understanding of relationship and transactional-based lending.     

Do capital flows drive credit growth and consumption in Central and Eastern Europe?

Domestic credit expansion in CEE economies, fuelled in part by foreign capital inflows, helped increase household welfare before the 2008 financial crisis caused a contraction across the region. How strong are the linkages between the current account, domestic credit and consumer spending? This study compiles a quarterly dataset of domestic credit as a share of GDP for 11 CEE European Union members and isolates structural breaks in the series’ growth rates that often align with the 2008 crisis. Vector autoregressive methods, particularly impulse response functions, show that increased current-account deficits lead to increased consumption in six of the 11 countries and increased credit growth in three, and that shocks to credit growth increase consumption in six countries. Capital inflows significantly increase consumption through domestic credit in Slovenia, while the Baltics show a large share of significant effects.