基于沃尔评分法的我国木材加工业上市公司财务绩效评价

用修正的沃尔评分法构建了财务绩效评价体系,对12家木材加工上市企业进行实证分析。得出结论:偿债能力是整个行业痛点,而林板一体化企业在偿债能力与发展能力具有显著优势。同时,木材加工行业在东部地区有集聚现象,但不具备可持续发展优势。     

Optimal probabilistic forecasts: When do they work?

Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Herein, we re-investigate the practice of using proper scoring rules to produce probabilistic forecasts that are ‘optimal’ according to a given score and assess when their out-of-sample accuracy is superior to alternative forecasts, according to that score. Particular attention is paid to relative predictive performance under misspecification of the predictive model. Using numerical illustrations, we document several novel findings within this paradigm that highlight the important interplay between the true data generating process, the assumed predictive model and the scoring rule. Notably, we show that only when a predictive model is sufficiently compatible with the true process to allow a particular score criterion to reward what it is designed to reward, will this approach to forecasting reap benefits. Subject to this compatibility, however, the superiority of the optimal forecast will be greater, the greater is the degree of misspecification. We explore these issues under a range of different scenarios and using both artificially simulated and empirical data.     

The analysis of unbalanced linear models with variance components

Statistical inference for fixed effects, random effects and components of variance in an unbalanced linear model with variance components will be discussed. Variance components will be estimated by Restricted Maximum Likelihood. Iterative procedures for computing the estimates, such as Fisher scoring and the EM-algorithm, are described.     

概念图评价研究述评

从概念图题型、概念图评分方法、概念图评价的功能与局限三个方面对概念图评价研究进行了综述,概念图评价研究在国外开展得较为成熟,而目前国内的研究明显地存在着理论研究难成体系、实证研究比较匮乏、研究阵容过于单薄等缺陷,国内应着力于研究队伍建设,大力开展实证研究,建立与完善切合我国教育实际的概念图评价的理论体系．     

Consumer Credit Risk Management in an Emerging Market： The Case of China

I. Introduction By any measure, China is now the world hottest economy, with an astonishing annual real GDP growth rate of 9 percent from 1991 to 2003 and with the world’s largest population of nearly 1.3 billion people. As one of the world’s largest and fastest growing economies,Consumer Credit Risk Management in China87 ?2006 The Authors Journal compilation ?2006 Institute of World Economics and Politics, Chinese Academy of Social Sciences there are tremendous opportunities for glo…     

A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring

Data mining techniques have numerous applications in credit scoring of customers in the banking field. One of the most popular data mining techniques is the classification method. Previous researches have demonstrated that using the feature selection (FS) algorithms and ensemble classifiers can improve the banks' performance in credit scoring problems. In this domain, the main issue is the simultaneous and the hybrid utilization of several FS and ensemble learning classification algorithms with respect to their parameters setting, in order to achieve a higher performance in the proposed model. As a result, the present paper has developed a hybrid data mining model of feature selection and ensemble learning classification algorithms on the basis of three stages. The first stage, as expected, deals with the data gathering and pre-processing. In the second stage, four FS algorithms are employed, including principal component analysis (PCA), genetic algorithm (GA), information gain ratio, and relief attribute evaluation function. In here, parameters setting of FS methods is based on the classification accuracy resulted from the implementation of the support vector machine (SVM) classification algorithm. After choosing the appropriate model for each selected feature, they are applied to the base and ensemble classification algorithms. In this stage, the best FS algorithm with its parameters setting is indicated for the modeling stage of the proposed model. In the third stage, the classification algorithms are employed for the dataset prepared from each FS algorithm. The results exhibited that in the second stage, PCA algorithm is the best FS algorithm. In the third stage, the classification results showed that the artificial neural network (ANN) adaptive boosting (AdaBoost) method has higher classification accuracy. Ultimately, the paper verified and proposed the hybrid model as an operative and strong model for performing credit scoring.     

创新型企业评选指标体系的构建——以厦门市创新型示范企业为例

根据国家"创新型企业试点工作实施方案",厦门市开展市级创新型企业试点工作,通过认定创新型示范企业,树立企业自主创新典范,推动企业加强自主创新能力建设。本文以厦门市创新型试点企业为例,构建厦门市创新型示范企业的评选指标体系,以促进技术创新引导工程的顺利开展。     

信用评级模型构建的统计学解读

不断累积的征信信息为判断借款人信用表现提供了数据支持,计算机技术的飞速发展为批量处理贷款申请提供了技术保障。金融机构利用统计方法建立信用评级模型,能够尽可能准确地挖掘违约借款人的信用特征,对借款人信用表现进行精准预判。从信用评级模型的概念入手,揭示信用评级模型的统计学本质,通过对比信用评级建模的输入端和输出端,即征信数据和信用评分卡,从统计学的视角解读建模过程中需要解决的数据离散化、特征选择、数据缺失、拒绝推断和数据不平衡五类技术难题。     

Firm size,credit scoring accuracy and banks’ production of soft information

Research on SME bank financing generally assumes that smaller firms are more opaque from a lender’s perspective. We propose that the discriminatory power of credit scoring models can be thought of as a proxy for firm opaqueness, given that when these models perform poorly, lenders must invest in the production of ‘soft information’ to supplement the financial data used in these models. Measuring the discriminatory power of probit default models across quintiles of the Irish SME size distribution, we show that our proxy for firm opaqueness increases monotonically as firms get smaller. This finding supports an assumption that is the starting point to a wide strand of literature on SME bank financing. Our findings can also be interpreted as providing an insight to the literature on the determinants of banks’ choice of lending technology. While smaller banks may, as found in a substantial previous literature, produce larger amounts of ‘soft information’ due to their organizational advantages, they may also do so out of necessity: hard-information-based default modelling is less effective among smaller firms, thereby forcing banks that lend to these borrowers to invest more in relationship banking technologies to retain competitiveness.     

On iterative procedures of asymptotic inference

Abstract  An informal discussion is given on performing an unconstrained maximization or solving non-linear equations of statistics by iterative methods with the quadratic termination property. It is shown that if a miximized function, e.g. likelihood, is asymptotically quadratic, then for asymptotically efficient inference finitely many iterations are needed.