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.     

导游专业计算机教学中任务性学习的研究与实践

素质教育的一个重要部分就是学生综合应用能力及探究能力的培养。笔者针对导游专业的特点,在计算机基础教学中引入任务性学习方式,改善了教学效果,提高了该专业学生的计算机综合应用能力及自主探究能力。     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

Operational excellence in a green supply chain for environmental management: A case study

Nowadays, organizations have started to become more conscious about the environment in their supply chain operations. The greening process has guided supply chain practices into new ways of thinking according to green standards. The assessment of the performance of green supply chain management (GSCM) requires a holistic view for the whole supply chain. In this context, given that becoming green in the operational side of activities is essential, the performance assessment of operational activities also requires a holistic view to be taken. In this paper, an attempt has been made to improve the performance of GSCM by examining and evaluating the green operational excellence of a hot dip galvanizing company. The framework includes several green operational excellence key criteria, namely, quality management, efficiency management, green production/manufacturing, eco‐packaging, and green design. First, the weights of the criteria and the respective measurements were found by fuzzy analytic network process. Then, the overall operational performance score was found by a weighted scoring method. Finally, both managerial and theoretical implications were suggested according to the outcomes and findings of the case study.     

Macroeconomic implications of modeling the Internal Revenue Code in a heterogeneous-agent framework

Tax policy analysis in heterogeneous-agent models typically involves the use of smooth tax functions to approximate complex present tax law and proposed reforms. In this paper, we explore the extent to which the tax detail omitted under this conventional approach has macroeconomic implications relevant for policy analysis. To do this, we develop an alternative approach by embedding an internal tax calculator into a large-scale overlapping generations model that, while conditioning on idiosyncratic household characteristics, explicitly models key provisions in the Internal Revenue Code applied to labor income. We find that for a debt-constant steady state analysis of a given tax policy change, both approaches generate similar policy-induced patterns of macroeconomic activity despite variation in the underlying patterns of household tax-preferred consumption and labor supply behavior. However, this variation in underlying behavior is associated with significant quantitative and qualitative differences in macroeconomic aggregates along a debt-financed transition path immediately following a policy change. Consequentially, although the use of unconditional smooth tax functions may be a reasonable modeling simplification for steady state analysis of tax policy, caution should be taken for their use in transition path analysis within heterogeneous-agent models.     

Extending variety seeking to multi-sided platforms: Impact of new retailer listing

This study uses theories of variety seeking to examine how a new retailer listing on a multi-sided platform (MSP) affects consumer buying behavior. Using a unique dataset from an online MSP, we evaluate changes in purchase frequency with individual retailers after a new one is listed. Contrary to expectations, the results indicate that consumers increase their purchase frequency at existing retailers. Moreover, compared to consistency seekers, variety-seeking consumers show a higher positive effect as they buy even more. This result is observed for retailers in both competing and non-competing product categories. Finally, we discuss the theoretical contributions and implications for MSPs and retailers.     

基于校园网的流水阅卷模板设计

在校园局域网上,利用Excel电子表格设计的一套用于流水阅卷的成绩统计及分析模板,可以高效率地完成流水阅卷、计分和成绩分析等庞大的工作。该模板设计思路巧妙,操作过程方便,适用于各类学校的上机考试或传统纸质考试。     

基于AHP法的建筑公司财务风险评价

针对建筑公司财务管理的现状,提出了在财务管理中应用AHP法进行财务风险评价,同时结合模糊数学理论,构建了一个财务风险评价定性化与定量化结合的指标体系,并基于AHP法对建筑公司进行了财务风险评价。     

Spatial modelling of claim frequency and claim size in non-life insurance

In this paper, models for claim frequency and average claim size in non-life insurance are considered. Both covariates and spatial random effects are included allowing the modelling of a spatial dependency pattern. We assume a Poisson model for the number of claims, while claim size is modelled using a Gamma distribution. However, in contrast to the usual compound Poisson model, we allow for dependencies between claim size and claim frequency. A fully Bayesian approach is followed, parameters are estimated using Markov Chain Monte Carlo (MCMC). The issue of model comparison is thoroughly addressed. Besides the deviance information criterion and the predictive model choice criterion, we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. We give an application to a comprehensive data set from a German car insurance company. The inclusion of spatial effects significantly improves the models for both claim frequency and claim size, and also leads to more accurate predictions of the total claim sizes. Further, we detect significant dependencies between the number of claims and claim size. Both spatial and number of claims effects are interpreted and quantified from an actuarial point of view.