Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

In the city: The John Lewis partnership and planned shopping centres

A defining feature of large-scale retailing during the period 1950–1980 was the emergence and evolution of planned shopping centres. During the 1950s, department stores in the United States were in the vanguard of this phenomenon. In contrast, British department stores continued operating from traditional high street sites, and had limited opportunities for expansion within planned shopping centres until the 1970s. This paper addresses the connection between department store retailing and the development of the planned shopping centre in Britain from the perspective of one enterprise: the John Lewis Partnership. The article demonstrates that the Partnership was willing to operate department stores within centrally located shopping centres, but was circumspect about operating stores in non-centrally located shopping centres.     

企业边界人员的私人关系与企业绩效——基于中国市场数据的元分析检验

企业边界人员与外部利益相关者发展私人关系是新兴市场中的普遍现象，但是“私人关系”是否真的会提升企业绩效？当前文献对此问题并未给出一致结论。文章基于105篇使用中国市场数据的中英文文献，运用元分析技术（Meta-Analysis)考察了私人关系对企业绩效的影响，并分析了企业所处的制度环境和市场环境，企业的生命周期、规模、所有制形式等内部特征对上述影响的调节作用。通过对392个效应值的集成分析，文章发现，尽管私人关系确实能提升企业绩效（ｒ=0.167），但是不同层面的私人关系（政治联系和商业联系）对不同类型的企业绩效（财务绩效、运营绩效和战略绩效）所产生的影响存在差异。同时制度环境、市场环境、企业特征均会显著地调节私人关系对企业绩效的影响。上述结论为新兴市场中的企业如何将私人关系转化为企业绩效提供了指导。     

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.     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

Effects of export product diversification on quality upgrading: an empirical study

This paper empirically examines the effects of export product diversification on overall export quality in a panel data set of 115 countries from 1970 to 2010. It uses the data sets of the overall export quality and three export diversification measures of the International Monetary Fund: the extensive margin (variation in the number of new products exported), the intensive margin (variation in export values among existing exports), and the overall (Theil) index. It finds that export quality has only been increasing with a higher variation in export values among existing exports in low- and lower-middle-income countries. It also observes that export quality has been increasing with both a higher variation in export values among existing exports and new products exported in upper-middle- and high-income countries. The results are robust to the changing measures of controls in the benchmark model, the inclusion of many other controls; i.e. various measures of globalization, country size, factor endowments, macroeconomic stance, etc., and the exclusion of outliers.     

Multigroup validation of the service quality,customer satisfaction and performance links in higher education

The notion of ‘students as customers’ continues to be prominent, yet is insufficiently explored, especially from the standpoint of various stakeholders. To address this inadequacy, the present study employs a multigroup analysis of the service profit chain (SPC) model in higher education (HE). Its purpose is to examine the complete SPC model regarding stakeholder perceptions in order to inform its validation and implementation. A cross-sectional survey was employed in order to enable multigroup comparison of a comprehensive research model on subsamples of employees and students by means of structural equation modeling (SEM). Results provide support for the proposed SPC model within the sample of vocational colleges. Besides strongly linked constructs (quality–satisfaction–loyalty), some notable weaknesses (cracked ‘satisfaction mirror’) are found. Multigroup analysis also indicates some important differences between employees and students. Implications are provided for strategic service management in HE, which should acknowledge the differences among stakeholder perceptions.     

新发展格局视野中的消费券发放问题与重构研究

2020年面对新冠肺炎疫情冲击,各地纷纷发放消费券刺激消费。本轮发放消费券的省市和规模远超以往。考察当前各地消费券的实践,其存在三方面的问题:短期刺激消费与构建发展新格局缺乏衔接、地方各自为政与顶层设计阙如、财政补贴与财政约束的张力。针对这些问题,本文从新发展格局视野提出将当前消费券重构为财政和金融融合的消费金融券,使之成为构建新发展格局中扩大内需的长期政策和重要的宏观调控工具。消费金融券的制度设计要与社会结构新特征和新趋势相契合。针对低收入群体的消费金融券,应以财政补贴为主、以重大生活项目消费的利息补贴与信用担保为辅。针对中等收入群体的消费金融券,应以利息补贴为主,通过消费金融的方式扩大消费。加大消费金融券的全国顶层设计,明确中央和地方财政支持产业的范围和力度。