变量筛选后的个人信贷评分模型与统计学习

如何通过构建可靠的信用评分模型来评估贷款申请人的信用风险在信贷风险管理行业中发挥着重要的作用,这也成为学术界和商业界的一项重要研究课题。本文通过先筛选数据变量,然后再建立模型的方法,探讨了3种不同连接函数下广义线性模型的分类问题,并且当连接函数为logit (对应logistic回归模型)时,加入了自变量的交互项以优化模型。以德国UCI数据集为例,采用AUC和KS指标作为评价指标,与随机森林、支持向量机和XGBoost模型等目前信贷评分中最常用的统计学习模型进行了分析对比,结果表明:广义线性模型中logistic回归模型和SVM预测效果尚佳;对于连接函数为logit的广义线性模型,加入交互项后AUC和KS有所提高,表明交互项的加入优化了模型。     

基于面板logit模型的上市公司财务困境预测

目前关于财务困境预测的研究大多是局限于截面数据的静态计量和统计模型,忽视了公司的财务状况是不断变化的事实.为了揭示公司财务状况的变化过程,利用面板数据建立了panel logit概率模型.研究结果表明,panel logit模型在预测准确度方面优于普通的logit模型.     

GRNN和PNN模型在信用风险评估中的应用

广义回归神经网络GRNN和概率神经网络PNN,与传统的BP神经网络相比,收敛速度快,学习能力强.本文将其应用到信用风险评估,选取1057组公司财务数据作为训练数据,350组数据作为测试数据,分别建立基于不同属性的模型对样本公司财务状况评判其是守信公司还是违约公司,最终选取精度较高的作为最终模型对财务系统进行预测.结果表明,PNN对于信用风险评估泛化能力好,测试集正确率高,因此可以用作风险预警的模型,给决策者提供智力支持.     

ROC曲线在累积比数logit判别模型效果评价中的应用

目的:应用ROC曲线图评价累积比数logit判别模型的效果。方法:介绍ROC分析的原理,并应用其对医学生睡眠质量的累积比数logit判别模型进行评价。结果:ROC曲线较凸,曲线下面积的可信区间不包括0.5,结论:采用ROC曲线来评价累积比数logit判别模型,综合了灵敏度和特异度两个指标,可以全面反映模型的判别效果,比较科学。     

大数据背景下分布式支持向量回归模型研究

为解决传统的支持向量回归模型在处理大规模数据时计算效率较低的局限,文章将交互有效方法与支持向量回归模型相结合,提出了基于交互有效方法的分布式支持向量回归模型(CE-SVR).该模型首先采用分布式存储方式将大规模数据随机分配给多台机器,其次采用交互有效方法构建支持向量回归的近似损失函数替代全局损失函数获得近似预测结果,能够有效地分析大规模数据.数值模拟和应用研究的结果表明:在线性模型中,文章所提出模型的预测性能与全局支持向量回归模型基本一致,且显著优于基于单轮型方法的分布式支持向量回归模型(OS-SVR);在非线性模型中,文章所提出模型的预测性能会随着机器数的增加而降低,但其预测性能显著优于OS-SVR模型.     

误差非线性回归模型基于几何方法的若干二阶渐近性质

该文用微分几何方法对AR(q)误差非线性回归模型若干二 阶渐近性质进行了研究. 作者基于Fisher信息阵在欧氏空间定义了内积，并在期望参数空间建立了几何结构. 基于上述几何结构，给出了AR(q)误差非线性回归模型若干二阶渐近性质的曲率表示. 将前人的一些结果推广到AR(q)误差非线性回归模型.      

一种基于局部回归的多尺度碳市场价格预测模型研究

碳市场价格呈现非线性、非平稳的复杂特性,准确预测具有较大的挑战。基于“分而治之”的思想,提出了一种基于局部回归的多尺度碳市场价格预测模型。提出的模型利用集成经验模态分解(EEMD)对碳市场价格时间序列进行分解。启发于EEMD局部特征分解的特点,对分解后的分量采用局部回归方法进行预测,然后将分量预测结果进行集成。采用的局部回归方法包括局部线性回归(LLP)、局部多项式回归、局部岭回归、局部主成分回归、局部偏最小二乘回归和局部套索回归。实验结果表明基于局部回归的多尺度预测模型具有优异的预测性能。在提出的模型中,EEMD-LLP结构简单且性能更为突出,进一步对EEMD-LLP参数的适应性进行探讨。与新近提出模型的对比结果表明了EEMD-LLP在碳市场价格预测中的有效性。     

基于模糊积分的金融数据建模方法

以非可加模糊测度代替经典可加测度,基于模糊积分建立非线性回归模型是新近出现的数据建模方法.该方法充分考虑自变量因素之间的信息熔合(含协同或冲突)作用.本文完整地给出了适用于实数范围内的基于模糊积分(含Choquet积分和(S)ipo(s)积分)的多元非线性回归模型转化为普通线性回归模型的非线性转换方法及其简化算法.并将该方法应用于金融市场数据分析,结果表明效果较之普通多元线性回归有大的提高,且方法简便容易应用.     

基于L_(1/2)正则化Logistic回归的上市公司财务危机预警模型

针对财务危机预警模型指标存在信息冗余及Logistic回归模型预测精度有待提高的不足,利用L_(1/2)范数惩罚技术优化Logistic回归模型,构建基于L_(1/2)正则化Logistic回归的上市公司财务危机预警新模型.通过以沪深股市制造业股票交易得到特别处理(Special Treatment, ST)公司和非ST公司为研究对象,对比研究传统Logistic回归和L_1正则化Logistic回归模型的预测结果,实证研究表明:通过L_(1/2)正则化的Logistic回归模型不仅可以实现参数估计和变量选择,而且具有更高的预测精度和泛化能力.研究体现了新模型对预警问题的合理性和优越性,为上市公司财务危机预警后续研究提供一定的借鉴.     

零膨胀广义泊松回归模型与保险费率厘定

在保险产品的分类费率厘定中,最常使用的模型之一是泊松回归模型.当损失数据存在零膨胀(zero-in flated)特征时,通常会采用零膨胀泊松回归模型.在零膨胀泊松回归模型中,一般假设结构零的比例参数φ为常数,不受费率因子的影响,这有可能背离实际情况.假设参数φ与费率因子之间存在一定关系,并在此基础上建立了零膨胀广义泊松回归模型,即Z IGP(τ)回归模型.通过对一组汽车保险损失数据的拟合表明,Z IGP(τ)回归模型可以有效地改善对实际数据的拟合效果,从而提高费率厘定结果的合理性.     

Logistic evolutionary product-unit neural networks: Innovation capacity of poor Guatemalan households

A new logistic regression algorithm based on evolutionary product-unit (PU) neural networks is used in this paper to determine the assets that influence the decision of poor households with respect to the cultivation of non-traditional crops (NTC) in the Guatemalan Highlands. In order to evaluate high-order covariate interactions, PUs were considered to be independent variables in product-unit neural networks (PUNN) analysing two different models either including the initial covariates (logistic regression by the product-unit and initial covariate model) or not (logistic regression by the product-unit model). Our results were compared with those obtained using a standard logistic regression model and allow us to interpret the most relevant household assets and their complex interactions when adopting NTC, in order to aid in the design of rural policies.     

Benchmarking state-of-the-art classification algorithms for credit scoring

In this paper, we study the performance of various state-of-the-art classification algorithms applied to eight real-life credit scoring data sets. Some of the data sets originate from major Benelux and UK financial institutions. Different types of classifiers are evaluated and compared. Besides the well-known classification algorithms (eg logistic regression, discriminant analysis, k-nearest neighbour, neural networks and decision trees), this study also investigates the suitability and performance of some recently proposed, advanced kernel-based classification algorithms such as support vector machines and least-squares support vector machines (LS-SVMs). The performance is assessed using the classification accuracy and the area under the receiver operating characteristic curve. Statistically significant performance differences are identified using the appropriate test statistics. It is found that both the LS-SVM and neural network classifiers yield a very good performance, but also simple classifiers such as logistic regression and linear discriminant analysis perform very well for credit scoring.     

Support vector machines for default prediction of SMEs based on technology credit

In Korea, many forms of credit guarantees have been issued to fund small and medium enterprises (SMEs) with a high degree of growth potential in technology. However, a high default rate among funded SMEs has been reported. In order to effectively manage such governmental funds, it is important to develop an accurate scoring model for selecting promising SMEs. This paper provides a support vector machines (SVM) model to predict the default of funded SMEs, considering various input variables such as financial ratios, economic indicators, and technology evaluation factors. The results show that the accuracy performance of the SVM model is better than that of back-propagation neural networks (BPNs) and logistic regression. It is expected that the proposed model can be applied to a wide range of technology evaluation and loan or investment decisions for technology-based SMEs.     

前馈神经网络和Logit回归的比较研究

近年来 ,前馈神经网络广泛地应用在 Logit回归作为标准统计方法的分析领域 .但却很少作它们之间的直接比较 ,本文是 Logit回归和前馈神经网络“比较研究”的一个尝试 ,说明了一些理论结果和特性 ,讨论了在它们的应用中碰到的一些实际问题 ,还进一步用分析的和模拟的两种方法研究了一些重要的渐近概念、过分拟合以及模型选择等问题 ,最后讨论并给出一些结论     

财务舞弊侦测——以中国上市公司为例研究

会计师事务所的独立性在世界范围内遭到了不同程度的质疑。本文搜集了1998-2009年间受到证监会处罚并经三大权威报纸披露的31家舞弊公司的信息,将其作为舞弊研究样本,另外整理了同行业同年度财务状况良好的31家非舞弊公司的信息,将其作为对照样本。通过归纳我国上市公司财务舞弊的具体特征,本文选取了与企业经营有关的相关财务指标和公司治理指标作为解释变量,采用逐步向前的多元逻辑回归法构建财务舞弊侦测模型,经验证,该模型侦测准确度为73.8%。     

Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis

In this paper, we present a general framework for understanding the role of artificial neural networks (ANNs) in bankruptcy prediction. We give a comprehensive review of neural network applications in this area and illustrate the link between neural networks and traditional Bayesian classification theory. The method of cross-validation is used to examine the between-sample variation of neural networks for bankruptcy prediction. Based on a matched sample of 220 firms, our findings indicate that neural networks are significantly better than logistic regression models in prediction as well as classification rate estimation. In addition, neural networks are robust to sampling variations in overall classification performance.     

Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time

This study attempts to show how a Kohonen map can be used to improve the temporal stability of the accuracy of a financial failure model. Most models lose a significant part of their ability to generalize when data used for estimation and prediction purposes are collected over different time periods. As their lifespan is fairly short, it becomes a real problem if a model is still in use when re-estimation appears to be necessary. To overcome this drawback, we introduce a new way of using a Kohonen map as a prediction model. The results of our experiments show that the generalization error achieved with a map remains more stable over time than that achieved with conventional methods used to design failure models (discriminant analysis, logistic regression, Cox’s method, and neural networks). They also show that type-I error, the economically costliest error, is the greatest beneficiary of this gain in stability.     

Predicting the survival or failure of click-and-mortar corporations: A knowledge discovery approach

With the boom in e-business, several corporations have emerged in the late 1990s that have primarily conducted their business through the Internet and the Web. They have come to be known as the dotcoms or click-and-mortar corporations. The success of these companies has been short lived. This research is an investigation of the burst of the dotcom bubble from a financial perspective. Data from the financial statements of several survived and failed dotcom companies is used to compute financial ratios, which are analyzed using three classification techniques—discriminant analysis, neural networks, and support vector machines to find out whether they can predict the financial fate of companies. Neural networks perform the task better than other techniques. Using discriminant analysis and neural networks, the key financial ratios that play a major role in the process of prediction are identified. Statistical tests are conducted to validate the findings.     

A comparison of neural networks and linear scoring models in the credit union environment

The purpose of the present paper is to explore the ability of neural networks such as multilayer perceptrons and modular neural networks, and traditional techniques such as linear discriminant analysis and logistic regression, in building credit scoring models in the credit union environment. Also, since funding and small sample size often preclude the use of customized credit scoring models at small credit unions, we investigate the performance of generic models and compare them with customized models. Our results indicate that customized neural networks offer a very promising avenue if the measure of performance is percentage of bad loans correctly classified. However, if the measure of performance is percentage of good and bad loans correctly classified, logistic regression models are comparable to the neural networks approach. The performance of generic models was not as good as the customized models, particularly when it came to correctly classifying bad loans. Although we found significant differences in the results for the three credit unions, our modular neural network could not accommodate these differences, indicating that more innovative architectures might be necessary for building effective generic models.     

Defensive financial decisions support for retailers in Greek pharmaceutical industry

In this paper, we present a decisions support solution designed for Greek pharmacies comprising a cash flow management system for early warning of financial distress and a financial advisor based on a neural network. The cash flow monitoring system integrates accounting elements with real time transactions and a predictive linear regression model while the decision support module is developed with the help of a neural network. For any given business unit the system associates accounting entries with information about credit times to reflect the precise instants of cash flows and using inflows/outflows equations monthly, eventually build its liquidity curve and cash flow balance over time. Alongside, a linear regression module is introduced to estimate future cash reserves based on past profitability ratios. Lastly, combining the power of artificial neural networks with expertise in this sector of pharmaceutical business, the financial decision support tool focuses on the retailers that face financial difficulties and suggests alternative solutions for escaping from distress and insolvency. The model has an ambitious and useful purpose, to inform and consult the owners of the business units and other members of the pharmaceutical chain, thus reduce financial risk for the chain.