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。     

企业数字化转型程度对融资成本的影响

近年来，大量企业基于数字化转型实现了弯道超车。选取2007—2021年A股上市公司数据，利用企业年报中数字关键词的词频来描述数字化转型程度，研究企业数字化转型程度对企业融资成本的影响。研究发现：企业数字化转型与融资成本显著负相关；检验延长时间窗口发现，在企业实施数字化转型后的一段时间内，数字化转型仍然降低了融资成本；根据异质性分析，数字化转型对非国有企业降低融资成本产生的效果更好，且东部地区企业深化数字化转型降低成本的作用更显著。     

Determinants of China's bilateral trade balance in global value chains

This paper identifies the determinants of China's bilateral trade balance using a new measure based on international input–output data, the so-called ‘trade in value-added’ (TiVA), which can prevent double counting in the estimation of bilateral trade balance. Our results show that using a measure based on gross exports, rather than TiVA, causes relatively large overestimation of the impact of the RMB exchange rate on China's bilateral trade balance. This overestimation is mainly because that the increasing production of exports may require increasing intermediate imports as a consequence of international fragmentation of production in global value chains. In addition, our results also show that the impact of FDI inflows on China's bilateral trade balances depends on the position and role of China and its trading partners in GVCs.     

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.     

Factors driving embodied carbon in international trade: a multiregional input–output gravity model

ABSTRACT

Concerns about the effects and consequences of climate change have notably increased in recent decades. Despite large advances in the understanding of this phenomenon, further research into the determinants of gas emissions is necessary, to shed light on the responsibilities of producers and consumers, and their potential contribution to mitigation strategies. This paper studies the trajectories and determinants of carbon embodied in world trade during a period of 15 years. Our methodology relies on a multiregional input–output model, environmentally extended. Drawing on data from the World Input–Output Database, we estimate embodied emissions in bilateral flows. Then, we assess the determinants of CO₂ emissions embodied in trade, combining input–output modelling with trade gravity panel data analysis. This paper offers a methodological approach that explains and quantifies the underlying factors of carbon trade, integrating the production and consumption perspectives and considering the geographical, structural and institutional context of countries.     

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.     

The exchange regime and trade balance in Turkey

Over the last 15 years, high trade deficits have become a source of external vulnerability for the relatively stabilized Turkish economy. This corresponds to the period where authorities have been following a floating exchange rate regime. Thus, this study aims to empirically show whether the adopted exchange rate regime has an impact on the trade balance for the period of 1987 Q1 to 2015 Q2. Estimation results indicate that there is a long-run relationship between the real effective exchange rate and trade balance under both fixed and floating regimes in Turkey, but there is no evidence for the J-curve hypothesis.     

Political Economy of Regional Integration: The Northeast Asian Model Revisited

It is found that the intra-regional trade share or functional integration plays an important role in the institutionalization of regional integration not only in the European Union (EU) but also in Northeast Asia. The crisis factor, which is measured by the regional economic growth rates, is empirically significant in Northeast Asia but not in the EU. This situation confirms the crisis model for Northeast Asia that emphasizes the stimulating role of crisis for the institutionalization of regional integration. However, the political leadership factor is not empirically significant in Northeast Asia and in the EU, and this finding does not support the political leadership model that emphasizes the facilitating role of political leadership for the institutionalization of regional integration.     

Information and Knowledge as Antecedents of Consumer Attitudes and Intentions to Buy and Recommend Fair-Trade Products

The authors propose and empirically test a causal model to understand how the availability of fair-trade information and consumer knowledge about this issue affect consumers’ attitudes and intentions toward fair-trade products. The model is built upon the attitude-behavior paradigm and the premises of agency theory. It is tested through structural equation modeling with a sample of 292 Spanish consumers. The findings are that consumers do not have good knowledge about fair trade and that this is significantly determined by the lack of information about this in the market. It is also observed that consumers’ perceptions about the availability of fair-trade information have negative effects on their concern about this issue and that such information as is available is not effective in reducing consumer skepticism. The research represents an extension of previous fair-trade literature because the role of information and communication in improving consumer attitudes and buying intentions has rarely been explored in the case of ethical products.     

内外融资条件、融资约束与企业绩效——来自京津冀地区企业调查的新证据*

基于独特的京津冀地区企业调查数据和克服内生性的结构方程模型，本文对融资约束的成因及影响进行了实证分析。分析结果表明，企业自身条件和外部市场环境均对融资约束有重要影响，且企业自身条件对融资约束的影响更强。总的融资约束指标对企业绩效的影响不显著。在区分融资约束的不同维度后，本文发现，融资可得性提高显著促进了企业绩效提升，融资频率增加却降低了企业绩效，而融资获得速度（等待时间）对企业绩效没有统计上的显著影响。进一步的分析表明，无论是正规融资渠道，还是非正规融资渠道，上述结论仍然成立。利用多种政策提高企业的管理水平，改善企业的自身条件，是缓解融资约束的最有效办法。