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.     

后疫情下休闲体育产业高质量发展的推进策略

新冠疫情以来,人们的健康意识觉醒,体育产业越来越受重视,本文通过对疫情期间体育产业受到的影响进行分析,从体育消费、体育产品中小企业、体育赛事几个方面阐述了疫情以来体育产业受到的阻碍。凡事都有两面性,疫情也为体育产业带来了新的机遇,"线上+线下"融合、新兴产业的崛起、人们健康意识的加强都因为疫情带来了新的发展动力。在后疫情时代,政府应该推出新的政策与措施鼓励体育产业发展,市场则应该抓住机遇实现体育产业的供给侧改革,通过与新兴产业、互联网等的结合,推动体育产业的转型升级。     

经济新常态下煤炭企业发展战略研究

着眼于当前我国煤炭企业的新环境,我们要转变视角,致力于探索推动我国煤炭行业发展的新途径和新方法,为了更好地保护矿工的合法权益,保障煤炭行业的可持续发展,煤炭企业需要引进一些创新性技术和先进管理理念。论文主要基于关心矿工生命安全和煤炭企业安全发展的角度,对目前我国煤炭企业的发展提出一些思考和策略。     

基于GM(1,1)模型的造纸业上市公司财务研究——以冠豪高新为例

财务风险是企业调整发展战略、提升管理水平的重要参考指标之一。财务风险预测模型的建立能够预测企业在中短期内的财务风险,为企业敲响警钟。以造纸业上市公司冠豪高新为例,选取近五年财务杠杆系数为风险指标,建立GM(1,1)风险预测模型,结果显示该公司短期内财务风险呈下降趋势,调查发现这主要得益于产品成本的降低以及营销策略的改变。     

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.