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。     

我国省级区域金融发展对经常项目的影响研究——基于货物贸易视角的分析

本文选取我国大陆31个省(市、自治区)2007年-2018年的面板数据,综合运用系统GMM和差分GMM估计方法,实证检验了沿海与内陆、沿边与非沿边、享受优惠政策高与低、金融控制强与弱等4类不同省区金融发展对经常项目余额的影响。研究结果表明:使用全部样本估计时,金融发展显著负向影响经常项目余额,这一结论经稳健性检验后依然成立。使用不同区域的子样本估计时,金融发展对沿海省区、非沿边省区、享受优惠政策高以及金融控制弱省区的经常项目余额的抑制作用较大,而在其他省区作用较小。因此,各地区应充分利用金融手段来调整进出口余额,以保证经常项目的持续平衡。     

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.     

A tale of two countries: Directed technical change,trade and migratory movements

We propose a North-South model that reconciles trade and production strategies, flows of innovators and the path of economic divergence, or convergence, between countries. We explain the mechanisms behind these forces and show how the technological and economic gaps can be reversed if southern countries stop imitating northern goods and, instead, produce complementary goods. Such a strategy of complementarity on production yields the necessary incentives to innovators to engage in research in southern countries, which enhances the catching-up process between countries. It is also shown that migratory movements of unskilled labor between countries are also relevant to understand the dangers and benefits of different trade strategies for economic growth. This paper suggests a positive (negative) correlation between technological innovation in the North (South) and the level of substitutability in production, while under complementarity, technological innovation catches up in the South, therefore fostering the economic catching up process. A positive correlation between inflows of skilled and unskilled labor and substitutability of production between countries is also verified.     

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.     

Dynamic response of bank-intermediated trade finance to a global liquidity shock

This paper investigates the bank-level responses of a bank-intermediated instrument of trade finance to a negative global liquidity shock in Korea. Using a factor-augmented vector autoregression approach, the results show that there exists significant heterogeneity in bank-level trade-finance responses to a global liquidity shock. In addition, we find that the source of the heterogeneity is the bank-level foreign currency liquidity condition; banks with a better foreign currency liquidity condition may dampen the negative impact of a global liquidity shock on trade-finance.     

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.