我国产险公司财务恶化预警——基于Logistic模型

如何准确评估保险公司财务并对财务恶化进行正确的预警．一直是国内外学界、业界及监管机构关注的热点问题之一。本文采用我国产险公司2002～2005年数据，通过Logistic模型找出对产险公司财务状况具有显着影响的各种因素．并利用事前概率与预测概率辨别出财务状况异常的产险公司，在错误成本允许的条件下，寻求最佳的监管资源配置。实证结果表明．在不同的监管标准下，衡量产险公司财务影响因素的显著程度有所差异。模型受检值以事前概率为标准较预测概率为好．且受检值的大小将会影响产险公司财务状况预测的结果。     

证券投资风险预测的E-Bayes估计

本文给出了E-Bayes方法，以上海证券个股五粮液52个连续交易日的收盘价格为例，建立数学模型进行分析和预测，预测结果与市场实际值相当吻合。与灰色系统理论中的GM（1，1）预测模型相比，本文提出的方法预测的精度更高，计算量小。不仅适用于经济系统的分析与预测，也适用于其它系统的分析与预测。     

Macroeconomic forecasting using structural factor analysis

The use of a small number of underlying factors to summarize the information from a much larger set of information variables is one of the new frontiers in forecasting. In prior work, the estimated factors have not usually had a structural interpretation and the factors have not been chosen on a theoretical basis. In this paper we propose several variants of a general structural factor forecasting model, and use these to forecast certain key macroeconomic variables. We make the choice of factors more structurally meaningful by estimating factors from subsets of information variables, where these variables can be assigned to subsets on the basis of economic theory. We compare the forecasting performance of the structural factor forecasting model with that of a univariate AR model, a standard VAR model, and some non-structural factor forecasting models. The results suggest that our structural factor forecasting model performs significantly better in forecasting real activity variables, especially at short horizons.     

天津物流业基础设施及发展水平统计分析

物流业是天津市重点发展的支柱型产业.本文旨在介绍天津物流业的现状,并对物流产业水平进行系统的统计分析,为宏观物流政策的制定提供依据.     

上市公司关联交易盈余管理实证研究

文章以科技类上市公司为研究样本,运用Logistic(逻辑斯蒂)回归模型对关联交易盈余管理的原因和特征进行了分析。研究结果显示:连续三年没有增发新股和配股的上市公司,其再筹资动机强烈;上市公司的大股东及其关联方占用上市公司大量资金;上市公司关联担保现象严重;大股东及其关联方不仅通过非主营业务管理盈余,而且还通过主营业务管理盈余。     

Technological change in energy systems: Learning curves, logistic curves and input-output coefficients

Learning curves have recently been widely adopted in climate-economy models to incorporate endogenous change of energy technologies, replacing the conventional assumption of an autonomous energy efficiency improvement. However, there has been little consideration of the credibility of the learning curve. The current trend that many important energy and climate change policy analyses rely on the learning curve means that it is of great importance to critically examine the basis for learning curves. Here, we analyse the use of learning curves in energy technology, usually implemented as a simple power function. We find that the learning curve cannot separate the effects of price and technological change, cannot reflect continuous and qualitative change of both conventional and emerging energy technologies, cannot help to determine the time paths of technological investment, and misses the central role of R&D activity in driving technological change. We argue that a logistic curve of improving performance modified to include R&D activity as a driving variable can better describe the cost reductions in energy technologies. Furthermore, we demonstrate that the top-down Leontief technology can incorporate the bottom-up technologies that improve along either the learning curve or the logistic curve, through changing input-output coefficients. An application to UK wind power illustrates that the logistic curve fits the observed data better and implies greater potential for cost reduction than the learning curve does.     

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.     

Application of logistic analysis to the history of physics

Recently, two analyses have tried to put technological progress in a larger context. One interpretation hypothesizes that technological progress is likely to continue at increasingly higher rates of change. Another interpretation, which includes data from the beginning of the universe to the present, suggests that the universe is approaching a transition point in a logistic development of complexity. This logistic development is similar to the way ideas or products diffuse in a population, i.e., the rate of discovery in a field of knowledge is proportional to the amount discovered and the amount to be discovered. To test a part of this hypothesis, a leading indicator field (fundamental physics) was identified and the events in the history of this field were analyzed. Twelve subfields were identified and grouped into six stages. Each stage seemed to demonstrate a logistic-like development. By analyzing both the median time of development and the characteristic time of development of these stages, the overall development of this one field was found to suggest logistic development. These data seem to indicate that development in fundamental physics is slowing down, with at least one subfield beyond string physics yet to be developed. The data tend to support the hypothesis that a knowledge field can develop logistically.     

The Delphi method, impeachment and terrorism: Accuracies of short-range forecasts for volatile world events

Two experiments examined the effects of iterated polling and feedback on prediction accuracy for volatile world events using the Delphi paradigm. In both experiments, participants predicted rapidly changing geopolitical events. The first experiment occurred during the litigation surrounding the Clinton/Lewinsky affair in 1998. Results indicated that feedback improved individual and group accuracy for predictions of whether an event would occur. These types of predictions derived from group consensus were approximately 90% accurate, which was significantly higher than the average individual participant's accuracy. Neither polling nor feedback had much effect on time course predictions. The second experiment occurred during the American military action against the Taliban regime in Afghanistan in 2001. In Experiment 2, participants were polled three times to determine if increasing the number of iterated polls would improve Delphi accuracy. The results showed that accuracy improved from the first to the second poll but not from the second to the third. The groups predicted whether these scenarios would occur with approximately 75% accuracy, which was significantly higher than the accuracy of any individual participant. Once again, the Delphi procedure did not enhance time course predictions in Experiment 2.     

基于时间序列模型在物流需求预测中的应用

本文采用时间序列模型进行物流需求预测，用实例从数据平稳性的判断，平稳化、标准化，建模，经模型的识别、定价、参数估计和检验，到预测及误差和置信区间的计算，详细地说明了时间序列模型在物流需求预测中是如何应用的。结果表明该ARMA模型能够较好地拟合并可获得较高的中短期预测精度。