Failure prediction of Chinese A-share listed companies : comparisons using logistic regression model and neural network analysis : a thesis presented in partial fulfilment of the requirements for the degree of Master of Business Studies in Finance at Massey University, Palmerston North, New Zealand

This study compares the relative prediction accuracy of corporate failure between two prediction methods –logistic regression model and neural network analysis– based on a sample of 3598 observations and companies data obtained from the Chinese A- Share market during the period 1991 to 2002. Seven criteria have been set up to define failure according to attributes of Chinese listed companies. Using forty financial ratios and seven misclassification cost ratios of Type I and Type II error, two models achieve ranges of minimal misclassification cost at optimal cut-off points for two years prior to business failure; The logistic regression model is slightly superior to neural network analysis. Compared with random prediction, both models are efficient. In addition, the study points out that Total Asset Turnover (TATR), Cash Ratio (CASR), Earning per Share (EPS), Total Debt to Total Asset (TDTA), Return on Assets (ROA) and the natual log of Total Market Value (MVLN) could be significant financial indictors of corporate failure. Results of the study have important implications in credit evaluation, internal risk control and capital market investment guidelines

A Continuously Growing Dataset of Sentential Paraphrases

A major challenge in paraphrase research is the lack of parallel corpora. In this paper, we present a new method to collect large-scale sentential paraphrases from Twitter by linking tweets through shared URLs. The main advantage of our method is its simplicity, as it gets rid of the classifier or human in the loop needed to select data before annotation and subsequent application of paraphrase identification algorithms in the previous work. We present the largest human-labeled paraphrase corpus to date of 51,524 sentence pairs and the first cross-domain benchmarking for automatic paraphrase identification. In addition, we show that more than 30,000 new sentential paraphrases can be easily and continuously captured every month at ~70% precision, and demonstrate their utility for downstream NLP tasks through phrasal paraphrase extraction. We make our code and data freely available.Comment: 11 pages, accepted to EMNLP 201

Estimation and model selection in generalized additive partial linear models for correlated data with diverging number of covariates

We propose generalized additive partial linear models for complex data which allow one to capture nonlinear patterns of some covariates, in the presence of linear components. The proposed method improves estimation efficiency and increases statistical power for correlated data through incorporating the correlation information. A unique feature of the proposed method is its capability of handling model selection in cases where it is difficult to specify the likelihood function. We derive the quadratic inference function-based estimators for the linear coefficients and the nonparametric functions when the dimension of covariates diverges, and establish asymptotic normality for the linear coefficient estimators and the rates of convergence for the nonparametric functions estimators for both finite and high-dimensional cases. The proposed method and theoretical development are quite challenging since the numbers of linear covariates and nonlinear components both increase as the sample size increases. We also propose a doubly penalized procedure for variable selection which can simultaneously identify nonzero linear and nonparametric components, and which has an asymptotic oracle property. Extensive Monte Carlo studies have been conducted and show that the proposed procedure works effectively even with moderate sample sizes. A pharmacokinetics study on renal cancer data is illustrated using the proposed method.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1194 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Core Competencies for Chinese Language Teachers in Taiwan: A Multiple Criteria Decision Making Approach

There were two research questions in this study: what are the most important competencies for Chinese language teachers; and what is the priority for the competencies? This study applied the DEMATEL to analyze competencies from 15 experts. The results showed that competencies were categorized into four dimensions. They were Culture, Instruction, Communication, and Professional Development. Each dimension had multiple criteria. There were three kinds of criteria under the dimension of Culture. They were ‘global awareness,’ ‘intercultural communication,’ and ‘Chinese culture.’ The dimension of Instruction had three kinds of criteria. They were ‘instructional perspective,’ ‘teaching method,’ and ‘assessment.’ There were three kinds of criteria under the dimension of Communication. They were ‘oral Chinese,’ ‘learner\u27s language,’ and ‘expression ability.’ Finally, the criteria of ‘Chinese grammar,’ ‘use of technology’, and ‘collaborate with colleagues’ were categorized under the dimension of Professional Development. Overall, the four dimensions from the literature review were divided into 12 criteria. In the aspect of four dimensions, the sequence in terms of the degree of influence was ‘instruction,’ ‘professional development,’ communication’, and ‘culture.’ In general, the ‘instruction’ was considered by the experts as a most important dimension of competency for Chinese language teachers. In the aspect of 12 criteria, the sequence in terms of the degree of influence was ‘instructional perspective,’ ‘teaching method,’ ‘Chinese grammar,’ ‘collaboration with colleagues’, ‘use of technology’, ‘oral Chinese,’ ‘intercultural communication,’ ‘learner\u27s language,’ ‘expression ability,’ ‘global awareness,’ ‘Chinese culture’, and ‘assessment.’ In general, the ‘instructional perspective’ was considered by the experts as a most important criterion of competency for Chinese language teachers. Findings can be used to improve the quality of Chinese language teachers in many ways

East meets west : literature for crosscultural understanding.

For almost two decades, literature, which had played an essential role in foreign language teaching in many countries for many years, has been excluded in language classrooms. In recent years, there has been an increasing interest among scholars and educators, in both China and the West, in reviving literature as a means of acquiring language proficiency. But, this revival has been rather slanted towards the linguistic and literary elements of literature, while the inherent socio-cultural value of literature has been little discussed or explored. This dissertation seeks to analyze the relationship between culture and literature in second language acquisition and to provide, through illustrations of literary texts, a theoretical framework for teaching literature with the aim of acquiring crosscultural understanding

Experimental analysis for the effect of dynamic capillarity on stress transformation in porous silicon

The evolution of real-time stress in porous silicon(PS) during drying is investigated using micro-Raman spectroscopy. The results show that the PS sample underwent non-negligible stress when immersed in liquid and suffered a stress impulsion during drying. Such nonlinear transformation and nonhomogeneneous distribution of stress are regarded as the coupling effects of several physical phenomena attributable to the intricate topological structure of PS. The effect of dynamic capillarity can induce microcracks and even collapse in PSstructures during manufacture and storage.This work is funded by the National Natural Science Foundation of China Contract Nos. 10732080 and 10502014

Assessing the Adequacy of Variance Function in Heteroscedastic Regression Models

Heteroscedastic data arise in many applications. In a heteroscedastic regression model, the variance is often taken as a parametric function of the covariate or the regression mean. This paper presents a kernel-smoothing based nonparametric test for checking the adequacy of such a postulated variance structure. The test does not need to specify a parametric distribution for the random errors. It has an asymptotical normal distribution under the null hypothesis and is powerful against a large class of alternatives. Numerical simulations and an illustrative example are provided