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。     

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.     

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。     

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.     

Interenterprise Credit and Adjustment during Financial Crises: The Role of Firm Size

Small and medium‐sized enterprises (SMEs) suffered a sharp contraction in their borrowing from banks during the Great Recession. Analyzing a large firm‐level database for European countries, the paper shows that trade credit amplified the liquidity squeeze on SMEs, with adverse effects on their real activity. SMEs sharply increased their net trade credit and thus transferred financial resources to larger firms. Given the large weight of SMEs in the economy of European countries, the liquidity squeeze of SMEs likely contributed to the depth of the output fall and the slow recovery in Europe during the Great Recession.     

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.     

Ownership discrimination and private firms financing in China

This study examines the financing/funding of private firms in China. Our results show that private firms are significantly less funded through formal financing channels such as bank loans than state-owned firms, and hence have to resort to alternative financing such as trade credit. Consistent with the theoretical expectation and literature, there is a substitution effect between trade credit and bank loans for private firms, but this effect is much weaker compared to that of state-owned firms. Moreover, while the univariate comparisons indicate that private firms obtain more notes payable than state-owned firms, the multivariate regression analyses show that the relation between bank loan and notes payable is positive and indifferent between private and state-owned firms.     

The mechanism of credit risk contagion among internet P2P lending platforms based on a SEIR model with time-lag

The credit risk contagion of Internet peer-to-peer (P2P) lending platforms is an important part of Internet financial risk management and supervision. This study analyzes the contagion path of credit risk in Internet P2P lending. Based on complex network theory and the theory of infectious disease dynamics, the characteristics of Internet P2P lending development are combined to construct a SEIR model of credit risk transmission among Internet P2P lending platforms with time lag, and the robustness of the model is analyzed and proven. The influence of platform correlations, the susceptible immune rate, the platform elimination rate, contagion latency, the saturation coefficient, and the susceptibility input rate on credit risk contagion behavior among Internet P2P lending platforms is analyzed, using the equilibrium point and threshold value. The impact of each variable is analyzed by simulation. Corresponding countermeasures and suggestions are proposed to prevent and control credit risk contagion among these platforms.     

浅析区块链技术对小微企业信用认证及融资的作用

随着经济社会的全面发展,经济全球化已经成为全新的发展趋势,紧随其后的是后金融危机时代的到来。区块链是一种特定的数据技术,它在安全和便捷方面一直被业内人士所看好。论文围绕区块链技术对小微企业信用认证及融资的作用展开讨论分析,通过讨论分析有助于实现小微企业成功融资,为我国小微企业提供强有力的保障与支撑。