Non‐linear forecasts of stock returns

Following recent non‐linear extensions of the present‐value model, this paper examines the out‐of‐sample forecast performance of two parametric and two non‐parametric nonlinear models of stock returns. The parametric models include the standard regime switching and the Markov regime switching, whereas the non‐parametric are the nearest‐neighbour and the artificial neural network models. We focused on the US stock market using annual observations spanning the period 1872–1999. Evaluation of forecasts was based on two criteria, namely forecast accuracy and forecast encompassing. In terms of accuracy, the Markov and the artificial neural network models produce at least as accurate forecasts as the other models. In terms of encompassing, the Markov model outperforms all the others. Overall, both criteria suggest that the Markov regime switching model is the most preferable non‐linear empirical extension of the present‐value model for out‐of‐sample stock return forecasting. Copyright © 2003 John Wiley & Sons, Ltd.     

The Forecasting and Warning System of Geological Disasters in China

Geological disasters such as landslide and mudslide can be caused by many factors. Collaborations among different governmental agencies and multi-disciplines are necessary to establish a forecasting and warning system of geological disasters ( FWSGD). A FWSGD in China has been in operation since June 1 , 2003 as a joint project between the China Meteorological Administration (CMA) and the Ministry of Land and Resources ( MLR). This system has successfully shown very good social and economic benefits. The temporal-spatial distributions of China geological disasters and their causes have been analyzed in this paper. The FWSGD is described and its possible existing issues are also discussed. Authors suggest a new approach to study these disasters from interactions of the earth systems. Finally, a monitoring, forecasting, warning and preventing system for geological disasters in China is proposed.     

深部煤层开采高承压水突水预报及控制

以煤矿沉积岩体的工程地质、水文地质结构为切入点,利用地震和地质雷达技术实现软弱结构面的准确空间定位和岩体结构的精细探测。在 GIS 的支持下,建立煤层底板突水预测模型,提出底板突水评价指标体系,为煤矿深部开采提供系统的理论基础和安全可靠的技术方法。采用先进工艺的注浆堵水技术,对高承压水实施封堵,堵水效果 100%。为深部煤层高承压含水层上安全开采提供了一套较成功的预报和控制技术。     

灰色预测理论在火灾损失预测中的应用

简述了灰色理论中灾变预测的特点和其建模过程,并将其应用于湖南省火灾损失预测之中,并利用关联度分析检验了所建火灾损失预测模型的精度,经检验模型的分辨率比较高,同时预测值和原始值的曲线图也反映了这一点,因此模型的预测结果比较可靠,对于消防部门火灾预防有一定帮助.图1,表2,参8.     

定性预测与定量预测的综合运用研究

定性预测和定量预测各有其优点和缺点。这里介绍了它们各自的特点，并介绍了3种综合运用定性预测和定量预测的方法，在此基础上通过具体事例解释了它们的应用，然后比较了3种综合方法的相对适用情形。     

一种新的混合智能预测模型及其在故障诊断中的应用

针对机电设备运行状态受多因素影响且变化趋势复杂、难以用单一预测方法进行有效预测的问题，提出了一种新的基于经验模式分解、支持向量机和自适应线性神经网络的混合智能预测模型．首先，利用经验模式分解方法将非平稳时间序列按其内在的时间特征尺度自适应地分解为多个本征模式分量，然后根据这些分量各自趋势变化的剧烈程度选择合适的核函数，用支持向量机对其进行预测，最后通过自适应线性神经网络对这些预测分量进行自适应加权组合，得到原始序列的预测值．研究结果表明，对于标准算例和某机组振动趋势的预测，不论是单步预测还是多步预测，该模型的预测性能均好于单一的支持向量机预测方法。     

Do seasonal unit roots matter for forecasting monthly industrial production?

We investigate the seasonal unit root properties of monthly industrial production series for 16 OECD countries within the context of a structural time series model. A basic version of this model assumes that there are 11 such seasonal unit roots. We propose to use model selection criteria (AIC and BIC) to examine if one or more of these are in fact stationary. We generally find that when these criteria indicate that a smaller number of seasonal unit roots can be assumed and hence that some seasonal roots are stationary, the corresponding model also gives more accurate one‐step‐ahead forecasts. Copyright © 2004 John Wiley & Sons, Ltd.     

Intrinsic bubbles revisited: evidence from nonlinear cointegration and forecasting

This paper offers strong further empirical evidence to support the intrinsic bubble model of stock prices, developed by Froot and Obstfeld (American Economic Review, 1991), in two ways. First, our results suggest that there is a long‐run nonlinear relationship between stock prices and dividends for the US stock market during the period 1871–1996. Second, we find that the out‐of‐sample forecasting performance of the intrinsic bubbles model is significantly better than the performance of two alternatives, namely the random walk and the rational bubbles model. Copyright © 2004 John Wiley & Sons, Ltd.     

给水管网漏损检测周期的优化求解

城市供水企业迫切需要加强给水管网漏损管理,减少漏损水量,提高经济效益.给水管网检测周期的优化求解以管网历史漏损统计数据为基础,以漏损控制总费用最低为目标,采用自回归滑动平均混合过程及叠合模型预测管网漏水量、漏损件数,并求解管网经济漏水量,在此基础上建立漏损检测周期的优化数学模型.结果显示,总漏水量因实施优化检测周期而减少,漏损控制费用降低.优化检测周期的确定受管线检测费用、漏损水量费用等模型参数影响.     

LD钢电火花线切割加工工艺规律试验研究

研究ＬＤ钢电火花线切割加工的基本工艺规律，及其工艺参数与工艺指标之间的关系，得出生产率指标和表面粗糙度指标的预报模型，为优选ＬＤ钢的电火花线切割加工参数提供依据．