水利建设市场主体信用评价指标体系改进——以施工单位为例

针对现行水利建设市场主体信用评价指标体系,以施工单位为例,在比较分析已有信用评价结果的基础上,采用问卷调查统计方法,得到市场主体对现有评价指标的认同度,并分析不同指标认同度差异的原因,进而提出删除门户网站及平台信息、固定资产占总资产比例及施工年产值等认同度低的指标,提高管理层素质、人员素质及管理制度等认同度高的指标的权重。     

Recognition versus disclosure of finance leases: Evidence from Japan

This study examines whether credit market participants—bond investors and credit rating agencies—treat recognized and disclosed finance leases differently when assessing firms’ credit risk in Japan. I use firms’ credit risk, measured by bond spreads and credit ratings, to investigate the relations between recognized versus disclosed finance lease obligations and firms’ credit risk following the adoption of Statement No. 13, Accounting Standard for Lease Transactions. For a sample of firms issuing new bonds, I find that, unlike recognized finance leases, disclosed finance leases are not associated with bond spreads. Moreover, the associations between recognized versus disclosed finance leases and bond spreads are substantially different. Conversely, recognized and disclosed finance leases are associated with credit ratings and are processed similarly when credit ratings are determined. Taken together, my results suggest that the sophistication of capital market participants influences their credit risk assessments of recognized versus disclosed finance leases.     

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.     

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.     

A destination imagery processing model: Structural differences between dream and favourite destinations

The present paper aims at understanding how destination imagery is processed in tourists’ working memory. The research focuses on two highly desirable, but contrasting, destination brand categories: Favourite Destination, which involves retrospective memory of positive experiences in situ; and Dream Destination, based on tourists’ prospective memory. Through an online multilingual survey, 23,446 responses consisting of perceptions, evoked by way of free-recall, associated to Dream and Favourite Destinations, were collected. Findings point to clear structural differences between the imagery of Dream and Favourite Destinations. Additionally, a theoretical destination imagery model that offers a basis for future studies is proposed, and some managerial implications of significant relevance to destination marketing are discussed.     

The Effect of Media Coverage on the Pricing of Seasoned Equity Offerings

This paper examines the role of media coverage and investor attention on the outcomes of seasoned equity offerings (SEOs). I use an archive of Thomson Reuters news articles to proxy for firm visibility and investor attention. I find that the volumes of news articles prior to the offerings are positively associated with the offer price discounts of SEOs. Furthermore, the volumes of news articles are negatively associated with the cumulative abnormal returns three days around the SEOs. I conclude that the costs of equity increase with media coverage prior to SEOs. Overall, the evidence is consistent with the hypothesis that media coverage affects investors' information processing in SEOs. Copyright © 2018 ASAC. Published by John Wiley & Sons, Ltd.     

严监管背景下农信社合规风险管理体系建设

2018年,银行业监管环境进一步趋严,不但强调对银行的监管,更提出了对监管人员职责的监管,这种严格的外部监管环境对农信社的合规风险管理带来了极大的挑战。农信社合规风险管理体系的建设和完善已势不容缓。论文提出了农信社合规风险管理体系建设要从战略定位、文化渗透、防线搭建、专业专职化、考核和追究六个维度入手,推进合规风险管理长效机制的建设。     

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.     

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.