中小企业成长性评价方法有效性研究--来自沪深股市的实证

成长性研究是中小企业理论研究中的一个重要方面,而采用何种方法对中小企业的成长性进行评价以及评价有效性的检验又是此类研究的重要步骤.本文将分别通过突变级数法和灰色关联度分析法(Grey Relation Analysis,简称GRA)对沪深股市中小上市公司成长性进行实证研究,并建立回归模型以检验两种方法评价结果的有效性.     

铁路物流园区规划研究

随着物流业在中国的兴起和运输行业竞争的加剧,必然要求将物流运作理念引入铁路运输业。由此对大型铁路货运站改建为铁路物流园区的规划设计进行了研究,提出了铁路物流园区的规划流程、作业区面积计算方法和园区管理建议。     

改进灰色关联分析法在物流园区选址中的应用

通过对物流园区选址的影响因素和物流园区选址常用的方法进行了具体的分析,将层次分析法和灰色关联分析法结合成改进灰色关联分析法,对物流园区的选址问题进行评价,从而得到最佳的物流园区建设方案。并通过实例说明该方法的有效性,能很好地解决物流园区的选址问题。     

Optimal Hedging of Prediction Errors Using Prediction Errors

Wind power energy has been paid much attention recently for various reasons, and the production of electricity with wind energy has been increasing rapidly for a few decades. One of the most difficult issues for using wind power in practice is that the power output largely depends on the wind condition, and as a result, the future output may be volatile or uncertain. Therefore, the prediction of power output in the future is considered important and is key to electric power generating industries making the wind power electricity market work properly. However, the use of predictions may cause other problems due to “prediction errors.” In this work, we will propose a new type of weather derivatives based on the prediction errors for wind speeds, and estimate their hedge effect on wind power energy businesses. At first, we will investigate the correlation of prediction errors between the power output and the wind speed in a Japanese wind farm, which is a collection of wind turbines that generate electricity in the same location. Then we will develop a methodology that will optimally construct a wind derivative based on the prediction errors using nonparametric regressions. A simultaneous optimization technique of the loss and payoff functions for wind derivatives is demonstrated based on the empirical data.     

Office Rent Determinants Utilising Factor Analysis—A Case Study for İstanbul

In recent studies, a wide range of variables has been suggested for modelling the variation in office rent. However, only a few of them are found to influence the explanatory power of the model significantly. Moreover, the significance of these variables varies from model to model, depending on the characteristics of the region or/and the model. It is well established that the regression model of complex phenomena do not perform well, unless the effects of all major determinants are adequately represented. It is also known that complex phenomena may involve a large number of variables, and linear regression models often becomes cumbersome as the number of variables increases. A practical solution to the problem may be to pre-select the significant variables, and leave the less influential ones out. An even better solution could be to include all or most variables, while incorporating the group effect of some variables into a reasonable number factor variables. This way, both the accuracy and practicality of the model can be sustained. Serving this purpose, ‘Factor Analysis’ has been employed in establishing the office rent model for the metropolitan area of ?stanbul. The results of four different versions of the model, using linear and non-linear regressions are discussed.     

Forecasting an accumulated series based on partial accumulation II: A new Bayesian method for short series with stable seasonal patterns

The problem of forecasting a time series with only a small amount of data is addressed within a Bayesian framework. The quantity to be predicted is the accumulated value of a positive and continuous variable for which partially accumulated data are available. These conditions appear in a natural way in many situations. A simple model is proposed to describe the relationship between the partial and total values of the variable to be forecasted assuming stable seasonality, which is specified in stochastic terms. Analytical results are obtained for both the point forecast and the entire posterior predictive distribution. The proposed technique does not involve approximations. It allows the use of non-informative priors so that implementation may be automatic. The procedure works well when standard methods cannot be applied due to the reduced number of observations. It also improves on previous results published by the authors. Some real examples are included.     

重庆市工业产业投资结构分析及实证研究

本文根据投资结构与产业结构之间的互相影响,互相制约的关系,运用灰色理论的方法,对区域投资结构的与产业结构关联度进行分析的方法。通过对重庆市工业产业投资结构的实证分析,提出了对重庆市工业产业投资结构的优化建议。     

信息时代下人才需求预测分析

信息时代的到来,对人才的需求和人才培养模式提出了更高的要求。根据人才需求的历史数据。针对这一情况,利用灰色预测理论和方法进行了预测分析,并进一步针对社会发展需求提出了人才培养对策。     

Data preprocessing and data parsimony in corporate failure forecast models: evidence from Australian materials industry

The present study, based on data for delisted and active corporations in the Australian materials industry, is an attempt to develop a systematic way of selecting corporate failure‐related features. We empirically tested the proposed procedure using three datasets. The first dataset contains 82 financial economic factors from the corporation's financial statement. The second dataset comprises 73 relevant financial ratios, which either directly or indirectly measure a corporation's propensity to fail, and are conciliated from the first dataset. The third dataset is a parsimonious dataset obtained from the application of combining a filter and a wrapper to preprocess the first dataset. The robustness of this preprocessed dataset is tested by comparing its performance with the first and second datasets in two statistical (logistic regression and naïve‐Bayes) and two machine learning (decision tree, neural network) classes of prediction models. Tests for prediction accuracies and reliabilities, using the computational (ROC curve, AUC) and the statistical (Cochran's Q statistic) criteria show that the third dataset outperforms the other two datasets in all four predicting models, achieving various accuracies ranges from 81 per cent to 84 per cent.     

中国城乡金融非均衡发展的度量与预测

从城乡金融存贷款额、城乡金融资产、城乡金融化水平等三方面对1978—2012年间我国城乡金融非均衡化现象进行全面测度,并运用灰色预测模型对未来我国城乡金融发展水平进行预测及检验,结果显示:从城乡金融发展的视角来看,我国金融发展仍然处于非均衡状态;在政策等环境不变的前提下,我国城乡金融发展绝对差异水平将继续扩大,但相对差异水平在逐渐缩小。