我国上市公司社会责任与企业价值关联性分析——以房地产开发与经营业为例

基于财务报告的指标体系、运用主成分分析的方法，对在我国深、沪上市的房地产开发与经营业公司企业价值（以托宾Q衡量）与所建指标体系之间的关系进行实证分析。分析结果表明：样本公司财务报告所反映的其社会责任履行情况与其企业价值相关关系显著，这一实证结果表明企业的社会责任履行对现代企业来说，已经不是一个可有可无的附属品，在企业生存和发展过程中发挥着越来越重要的作用。     

情商在体育教学中的作用

学校体育教学的现状与时代的要求尚有一定距离,体育教学的改革势在必行。阐述情商EQ在体育教学中所起的作用。     

资本结构、治理结构与中小企业价值研究——基于中小企业板的实证

采用深圳证券交易所上市的37家中小企业2004年年报数据为样本进行的实证分析表明,负债比率、流通股比例、管理层持股比例等指标与中小企业价值负相关,股权集中度与中小企业价值正相关。     

Approximation of density functions by orthogonal series with grouped data

Summary Letg(x) andf(x) be continuous density function on (a, b) and let {ϕ_j} be a complete orthonormal sequence of functions onL ₂(g), which is the set of squared integrable functions weighted byg on (a, b). Suppose that over (a, b). Given a grouped sample of sizen fromf(x), the paper investigates the asymptotic properties of the restricted maximum likelihood estimator of density, obtained by setting all but the firstm of the ϑ_j’s equal to0. Practical suggestions are given for performing estimation via the use of Fourier and Legendre polynomial series. Research partially supported by: CNR grant, n. 93. 00837. CT10.     

A hybrid ARIMA and support vector machines model in stock price forecasting

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising.     

RESEARCHING RELATIONSHIPS FROM AN ATTACHMENT PERSPECTIVE: THE USE OF BEHAVIOURAL,INTERVIEW, SELF‐REPORT AND PROJECTIVE MEASURES

Having demonstrated its credentials as one of the more elegant and robust conceptualisations of human relationships, attachment theory has considerable relevance to social work. Attachment‐based research has flourished over the past few years and it now includes an impressive array of studies across the lifespan. This article considers four different measures used in attachment‐based research, although more discussion is devoted to interview and projective measures because they specifically embrace a psychodynamic approach in their design and analytic focus. The aim of the article is to provide a brief overview of each measure and to offer some illustrative examples using practice‐related material.     

The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility

It is well known that in finance variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high-frequency returns as the ex-post realization of the covariance matrix of low-frequency returns. For the analysis of dynamics of RCOV matrices, we propose the generalized conditional autoregressive Wishart (GCAW) model. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The GCAW is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments, and stationarity are discussed. Empirical examples including sequences of daily RCOV matrices from the New York Stock Exchange illustrate that our model outperforms the existing models in terms of model fitting and forecasting.     

Signal extraction goodness-of-fit diagnostic tests under model parameter uncertainty: Formulations and empirical evaluation

We present a time-domain goodness-of-fit (gof) diagnostic test that is based on signal-extraction variances for nonstationary time series. This diagnostic test extends the time-domain gof statistic of Maravall (2003 Maravall, A. (2003). A class of diagnostics in the ARIMA-model-based decomposition of a time series. Memorandum, Bank of Spain. Available at http://www.bde.es/servicio/software/tramo/diagnosticsamb.pdf [Google Scholar]) by taking into account the effects of model parameter uncertainty, utilizing theoretical results of McElroy and Holan (2009 McElroy, T., Holan, S. (2009). A local spectral approach for assessing time series model misspeci?cation. Journal of Multivariate Analysis 100:604–621.[Crossref], [Web of Science ®] , [Google Scholar]). We demonstrate that omitting this correction results in a severely undersized statistic. Adequate size and power are obtained in Monte Carlo studies for fairly short time series (10 to 15 years of monthly data). Our Monte Carlo studies of finite sample size and power consider different combinations of both signal and noise components using seasonal, trend, and irregular component models obtained via canonical decomposition. Details of the implementation appropriate for SARIMA models are given. We apply the gof diagnostic test statistics to several U.S. Census Bureau time series. The results generally corroborate the output of the automatic model selection procedure of the X-12-ARIMA software, which in contrast to our diagnostic test statistic does not involve hypothesis testing. We conclude that these diagnostic test statistics are a useful supplementary model-checking tool for practitioners engaged in the task of model-based seasonal adjustment.     

Parameter Estimation of Autoregressive Models Using the Iteratively Robust Filtered Fast-τ Method

Utilizing time series modeling entails estimating the model parameters and dispersion. Classical estimators for autocorrelated observations are sensitive to presence of different types of outliers and lead to bias estimation and misinterpretation. It is important to present robust methods for parameters estimation which are not influenced by contaminations. In this article, an estimation method entitled Iteratively Robust Filtered Fast? τ(IRFFT) is proposed for general autoregressive models. In comparison to other commonly accepted methods, this method is more efficient and has lower sensitivity to contaminations due to having desirable robustness properties. This has been demonstrated by applying MSE, influence function, and breakdown point criteria.     

An Individual Mixed‐Evaluation Method for Career Intervention

Economic issues linked to career counseling are a cause for concern to policy makers in developed countries because they expect career practitioners to provide evidence of the efficiency of career counseling interventions. The aim of this study was to test an individual evaluation method mixing time series (outcomes) and life narrative (processes). The method used 5 items related to 1 client's career decision self‐efficacy and studied the evolution of those items throughout the intervention of 1 career counselor (43 days). Changepoint analysis helped in identifying the changes that have to be taken into account for time series and which are contextualized in the client's verbatim analysis. This mixed method highlighted that the career counselor's intervention increased the client's career decision self‐efficacy. Practitioners could use the methodology proposed in this article to evaluate their interventions. They could also report their practice to clients, employers, and decision makers.