An effective going concern prediction model for the sustainability of enterprises and capital market development

The purpose of this study is to construct a two-stage effective and innovative going concern prediction model to predict going concern doubt for the sustainability of enterprises and capital market development. Samples of this study are the companies listed on the Taiwan Stock Exchange or the Taipei Exchange, totalling 196 companies and including 49 companies with going concern doubt and 147 normal companies (with no going concern doubt). The data are taken from the Taiwan Economic Journal (TEJ) and the Market Observation Post System during the period from 2001 to 2016 (totalling 16 years). This study adopts a two-stage way to construct the going concern prediction models. In Stage I, the traditional statistical method of stepwise regression (SR) and the data mining technique artificial neural network (ANN) are applied to select the important variables. In Stage II, two decision tree algorithms (data mining techniques): classification and regression tree (CART) and C5.0 are used to establish the prediction models. The results show that the SR + CART model has the highest going concern prediction accuracy, with an overall accuracy of 87.42%.     

灰色预测算法在物流园区需求预测中的应用

对物流园区需求预测进行了系统分析,介绍了灰色预测算法,和物流园区腹地划分方法;结合连云港港口物流园区需求预测的实例进一步阐述了灰色预测算法在物流园区需求预测中的应用.     

商业银行信用风险评估的生存分析模型及实证研究

企业发生财务危机，不能归还到期贷款是商业银行信贷资产的主要风险来源，商业银行如何构建恰当的信用风险评估模型来预测企业的财务危机，从而避免这类信用风险的出现就显得尤为重要。本文以我国上市公司为研究对象，结合杜邦分析法建立了基于生存分析的信用风险评估模型，模型对于随机选取的预测样本，其提前1年、2年和3年的预测准确率分别达到86％、72％和68％。通过与Ahman模型、Ohlson模型预测结果的比较和鲁棒性检验的结果发现，该模型同时具有可以使用时间序列、无需样本配对、中远期预测能力强和高鲁棒性的特点．这些特点特别对于商业银行中长期信贷风险管理具有较高的应用价值．     

金融危机研究模型与预测问题研究

本文回顾了金融危机形成理论的研究方法和研究模型,发现这些模型有两个重要的缺陷:即脱离了危机前宏观经济恶化的实际,同时把政府在经济恶化后的经济政策外生于模型之外,从而导致了危机理论只能解释危机而无法形成正确的预测。本文认为金融危机是市场缺陷在宏观经济恶化条件下的放大,是不适当的宏观经济政策的必然结果,现代金融条件下金融体系的脆弱性增加了金融危机爆发的频次和危害性。     

电力供需实验室的开发与建设

国家电网公司电力供需实验室是"十一五"期间国家电网公司重点建设的八大实验室之一。电力实验室由数据信息平台、业务支撑平台和应用平台3层平台构成。三者既相互独立,又相互联系。数据信息平台是电力供需实验室的基础。应用平台是直接面向用户、实现电力供需分析预测以及实验等功能的窗口,业务支撑平台是连接数据信息平台和应用平台的桥梁。目前,电力供需实验室已经基本具备了电力市场分析预测功能以及实验功能,可以应用于年度电力市场分析预测工作中。     

边疆民族地区人口结构预测及优化对策研究——以云南省为例

人口结构问题对边疆民族地区社会经济发展的制约作用越来越明显。对人口结构进行预测,把握人口结构发展趋势,制定人口结构对策对社会经济发展具有重要的理论和实践意义。优化云南人口结构的对策是：调整生育政策;发挥年龄结构优势使劳动适龄人口充分就业;利用主体功能区划进行人口布局的合理规划。     

县(市)级土地利用总体规划中耕地需求量预测方法及其应用

科学准确地预测耕地需求量,是制定县(市)级土地利用总体规划的基础.文章根据中国南方某县1996-2005年来的耕地变更调查统计数据,分别采用趋势分析法、回归分析法、指数平滑法、GM(1,1)模型法和灰色-马尔柯夫链模型的方法,对该地区耕地总量进行了模拟和预测.不同方法的预测结果表明,基于灰色-马尔柯夫链方法的预测结果更加准确可靠,更有利于土地管理决策者的经济决策行为.在当前耕地利用方式的背景下,灰色-马尔柯夫链模型可以作为县(市)级土地利用总体规划中耕地需求量预测方法的一种好选择.     

基于IAHP的流程重组预警指标体系设计

根据业务流程重组预警的实际需要,结合业务流程重组评价指标,构建了涵盖流程效率、资源利用率、流程成本及顾客满意度四个方面的业务流程重组预警的指标体系。最后用改进的层次分析法(IAHP法),对该指标体系进行了评价,得出了各指标对业务流程重组影响的权重。     

Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks

Corporate bankruptcy prediction has attracted significant research attention from business academics, regulators and financial economists over the past five decades. However, much of this literature has relied on quite simplistic classifiers such as logistic regression and linear discriminant analysis (LDA). Based on a large sample of US corporate bankruptcies, we examine the predictive performance of 16 classifiers, ranging from the most restrictive classifiers (such as logit, probit and linear discriminant analysis) to more advanced techniques such as neural networks, support vector machines (SVMs) and “new age” statistical learning models including generalised boosting, AdaBoost and random forests. Consistent with the findings of Jones et al. ( 2015 ), we show that quite simple classifiers such as logit and LDA perform reasonably well in bankruptcy prediction. However, we recommend the use of “new age” classifiers in corporate bankruptcy modelling because: (1) they predict significantly better than all other classifiers on both the cross‐sectional and longitudinal test samples; (2) the models may have considerable practical appeal because they are relatively easy to estimate and implement (for instance, they require minimal researcher intervention for data preparation, variable selection and model architecture specification); and (3) while the underlying model structures can be very complex, we demonstrate that “new age” classifiers have a reasonably good level of interpretability through such metrics as relative variable importances (RVIs).     

我国通信企业客户流失预测研究综述

客户流失问题是全球电信企业面临的一个严峻问题。我国通信市场经过快速发展,市场容量已经接近饱和,客户流失成为我国各通信运营商面临的严重问题。文章综述了客户流失的影响因素及其预测模型和方法,以期为深入研究客户流失问题提供参考。