Do investors in Green Bond market pay a premium? Global evidence

We examine the pricing difference of Green Bonds (GB) and conventional bonds (CBs) in capital markets worldwide. Credit spread is used to observe whether investors would like to pay a premium for GBs over CBs. This study uses panel data regression with hybrid model to analyse daily observations over the period 2016 to 2017. We employ Option-Adjusted spread (OAS) to measure the credit spreads of bonds while controlling for bond specific, macroeconomic and global factors that influence the spread. With the hybrid model used in the panel data analysis, we were able to capture the fixed-effects of variables in a random effect model. We find that GBs are traded at a premium of 63 basis points (BPS), compared with a comparable corporate bond issue. We find that the green label provides issuers an incentive to raise funds through issuing GBs while providing investors an opportunity to diversify their investments returns. Our findings provide several implications to the major stakeholders driving the GB market to scale up the market to finance the required level of global green investment needs. We stress an urgent need to support the growth of the GB market to achieve sustainable development through mitigating climate change challenges.

Abbreviation GB: Green Bond; CB: Conventional Bond; YS: Yield Spread; BPS: Basis Points; OAS: Option-Adjusted Spread; PCSE: Panels Corrected Standard Errors; CPI: Consumer Price Index; GBPs: Green Bond Principles; CBS: Climate Bond Standard     

考虑违约距离的上市公司危机预警模型研究

在上市公司财务危机预警中,违约距离起着重要的不可替代的作用,仅考虑财务指标并不足以完全解释企业财务危机发生的原因.文章应用Merton模型对2002～2004年ST公司和相应配对公司的危机发生之前的违约距离进行了研究,发现危机公司违约距离在危机发生前第三年明显低于正常公司的相应值,违约距离比传统财务指标能更早地预警到企业财务危机的发生.结合违约距离因素的危机预警模型能更明显地提高模型的危机判断正确率.     

宏观投资的影响因素与实证分析

在界定宏观投资的基础上，选择储蓄、价格、利润和利率等影响因素的代表变量，建立固定资产投资的理论模型；以1985－1999年为样本区间，采用SPSS统计分析软件包，对变量数据进行相关性分析和主成分分析，构建我国固定资产投资的回归模型；通过实证分析得出我国固定资产投资各影响因素的结论并给出相应的政策含义。     

基于主成分分析的多元线性回归用水量模型研究——以安徽省为例

用水量的准确预测对水资源规划具有重要意义。根据统计资料,选取了八项主要影响用水量变化的指标。按照主成分分析法原理,运用少数综合因子对影响用水量变化的原有八项指标进行了综合和简化,并研究其贡献率及综合效应;分析了用水量增长的特点,选用Logistic方程对综合因子进行回归分析;结合多元线性回归方法,建立了安徽省用水量模型;拟合结果表明,模型具有较高的精度;运用该模型,对安徽省2010年的用水量进行了预测;为水资源规划与管理提供参考依据。     

ML– and semiparametric estimation in logistic models with incomplete covariate data

ML–estimation of regression parameters with incomplete covariate information usually requires a distributional assumption regarding the concerned covariates that implies a source of misspecification. Semiparametric procedures avoid such assumptions at the expense of efficiency. In this paper a simulation study with small sample size is carried out to get an idea of the performance of the ML–estimator under misspecification and to compare it with the semiparametric procedures when the former is based on a correct assumption. The results show that there is only a little gain by correct parametric assumptions, which does not justify the possibly large bias when the assumptions are not met. Additionally, a simple modification of the complete case estimator appears to be nearly semiparametric efficient.     

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%.     

Bayesian assessment of dimensionality in reduced rank regression

We consider Bayesian inference about the dimensionality in the multivariate reduced rank regression framework, which encompasses several models such as MANOVA, factor analysis and cointegration models for multiple time series. The fractional Bayes approach is used to derive a closed form approximation to the posterior distribution of the dimensionality and some asymptotic properties of the approximation are proved. Finite sample properties are studied by simulation and the method is applied to growth curve data and cointegrated multivariate time series.     

中国电子商务企业生存的实证分析

本文通过Logisitc回归模型，对电子商务企业生存的影响因素进行了分析。笔者认为，电子商务企业的生存是受企业自身基因构成决定的，而电子商务企业的基因构成包括了企业家才能、资金、信息资本、人力资本、社会资本和技术资本。同时，企业的基因会随着环境的变化而不断地进行着调整和整合，以适应竞争的需要。实证分析的结果证实了笔者的判断。     

城市公交行业顾客满意度测评及其提升对策——以合肥市为例

城市公交作为城市经济运转的动脉,已经成为国内城市发展规划中的重中之重。然而,目前在城市公交行业占主体地位的国有公交企业却亟待提高营运效益,增强企业活力,以摆脱经营上的困境。立足于安徽省省会城市合肥市的公交行业,通过实证研究与规范研究相结合的方式,针对城市公交行业的顾客满意度测评及其提升对策问题进行了较为深入的探讨。     

A Semiparametric Method for Valuing Residential Locations: Application to Automated Valuation

This paper is motivated by automated valuation systems, which would benefit from an ability to estimate spatial variation in location value. It develops theory for the local regression model (LRM), a semiparametric approach to estimating a location value surface. There are two parts to the LRM: (1) an ordinary least square (OLS) model to hold constant for interior square footage, land area, bathrooms, and other structural characteristics; and (2) a non-parametric smoother (local polynomial regression, LPR) which calculates location value as a function of latitude and longitude. Several methods are used to consistently estimate both parts of the model. The LRM was fit to geocoded hedonic sales data for six towns in the suburbs of Boston, MA. The estimates yield substantial, significant and plausible spatial patterns in location values. Using the LRM as an exploratory tool, local peaks and valleys in location value identified by the model are close to points identified by the tax assessor, and they are shown to add to the explanatory power of an OLS model. Out-of-sample MSE shows that the LRM with a first-degree polynomial (local linear smoothing) is somewhat better than polynomials of degree zero or degree two. Future applications might use degree zero (the well-known NW estimator) because this is available in popular commercial software. The optimized LRM reduces MSE from the OLS model by between 5 percent and 11 percent while adding information on statistically significant variations in location value.