Out‐of‐sample equity premium prediction: A scenario analysis approach

We propose two methods of equity premium prediction with single and multiple predictors respectively and evaluate their out‐of‐sample performance using US stock data with 15 popular predictors for equity premium prediction. The first method defines three scenarios in terms of the expected returns under the scenarios and assumes a Markov chain governing the occurrence of the scenarios over time. It employs predictive quantile regressions of excess return on a predictor for three quantiles to estimate the occurrence of the scenarios over an in‐sample period and the transition probabilities of the Markov chain, predicts the expected returns under the scenarios, and generates an equity premium forecast by combining the predicted expected returns under three scenarios with the estimated transition probabilities. The second method generates an equity premium forecast by combining the individual forecasts from the first method across all predictors. For most of predictors, the first method outperforms the benchmark method of historical average and the traditional predictive linear regression with a single predictor both statistically and economically, and the second method consistently performs better than several competing methods used in the literature. The performance of our methods is further examined under different scenarios and economic conditions, and is robust for two different out‐of‐sample periods and specifications of the scenarios.     

Scenario planning: An investigation of the construct and its measurement

Scenario‐planning academicians and practitioners have been observing for more than three decades the importance of this method in dealing with environmental uncertainty. However, there has been no valid scale that may help organizational leaders to act in practice. Our review of prior studies identifies some problems related to conceptualization, reliability, and validity of this construct. We address these concerns by developing and validating a measure of scenario planning based on Churchill's paradigm (Journal of Marketing Research, 1979, 16, 64–73). Our data analysis follows from a sample of 133 managers operating in the healthcare field in France. To validate our scale, we used three approaches: first, an exploratory factor analysis; second, an examination of psychometric proprieties of all dimensions; and third, a confirmatory factor analysis. The results of this study indicate that scenario planning is a multidimensional construct composed of three dimensions: information acquisition, knowledge dissemination, and scenario development and strategic choices.     

基于语义匹配的作战体系仿真想定生成方法

针对传统的想定建模方法耗时长、成本高的问题,为了实现作战体系概念模型数据到仿真想定的数据映射重用, 提出一种基于语义匹配的作战体系仿真想定生成方法。首先,基于网络爬虫技术获取武器装备的参数信息, 构建了作战领域知识库。在此基础上, 提出了新型的混合式语义匹配方法以提供仿真想定的数据来源, 根据自然语言数据库WordNet开展调参实验确定算法参数, 通过对比人工打分结果有效提高了算法的可信性。在确定了概念模型与仿真想定的数据映射关系后, 给出了仿真想定的生成方法。最后, 通过编队突防仿真应用案例, 验证了该方法的可行性。     

LLN中基于移动节点邻居探测的高效路由寻路算法

针对低功耗有损网络(low power lossy networks, LLN)移动性支持路由算法中移动节点(mobile node, MN)备选父节点集选取不合理、存在障碍物的中速场景下MN寻路不及时和路由度量单一等问题, 提出中速场景下MN邻居探测的LLN高效寻路(highly-efficient MN neighbor detection based pathfinding protocol for LLN, NDM-RPL)算法。提出链路质量检测机制, 通过计算安全阈值和危险阈值以便MN及时寻找下一个父节点(nextparent node, NPN)。其次, 提出基于变异系数的父本选择目标函数, MN选择NPN综合考虑了多种度量选出NPN。最后, 提出监听预选机制, 解决了备选父节点集选取不合理的问题。理论分析和仿真结果表明, NDM-RPL算法在MN能耗和控制开销等方面的性能均得到了有效提升。     

最终需求扩张对收入及收入差距的影响研究

基于投入产出模型从最终使用的角度分析了消费、投资和出口扩张对收入水平及收入差距的影响作用.研究结果表明,相比于扩大投资和出口,扩大消费需求不仅对提高收入水平有显著作用,而且对缩小收入差距有积极的作用.     

中国数字地球的发展思考

该文介绍了数学地球的内容、特点和作用 ,分析了建设中国数字地球的意义和面临的困难。在此基础上针对性地提出了建设中国数字地球的战略计划和实施方案。     

基于情景规划的跨国油气投资战略性风险评价

基于中国石油企业跨国油气投资的特点,确定决策风险、地缘政治风险、竞争风险、运营风险、社会责任风险等5种影响跨国油气投资战略性风险的关键因素,并引入情景规划方法对战略性风险进行情景假设,构建跨国油气投资战略性风险的评价模型。所建模型克服传统风险评价方法对风险随机选择与评价的不足,能根据模拟情境,重点评价重要影响的战略性风险及其风险状态,使决策者能更早地预防风险。研究成果对于当前复杂多变的环境下更好地防范和应对跨国油气投资风险具有重要的战略意义。     

基于情景生成的银行卡网络资金配置鲁棒优化模型

运用鲁棒优化理论和情景生成方法,研究了银行卡网络资金配置问题.在对银行卡网络资金需求分析的基础上,运用鲁棒优化理论建立了银行卡网络资金配置鲁棒优化模型.该模型为一个多目标规划问题,满足诸如尽可能全天候满足持卡人需求、系统的总收益最大、网络的鲁棒性等多个相互冲突目标.以我国金融市场为背景,通过仿真-优化-聚类情景生成方法...     

基于稀疏恢复的直接数据域STAP算法

在机载/星载雷达系统中,空时自适应处理(STAP)可有效抑制杂波并实现动目标检测。基于统计的STAP算法通过平稳的训练样本来估计检测单元内的杂波协方差矩阵,并设计相应的滤波器以提高检测单元的输出信杂比。但训练样本的平稳性在实际快变的杂波环境中无法保证,因而此类算法在实际非均匀杂波环境中性能较差。该文通过挖掘检测单元数据在角度-Doppler域上的稀疏性,提出一种新的直接数据域STAP算法。该算法通过稀疏恢复来获得检测单元的高分辨空时谱估计,有效地避免杂波旁瓣对目标检测的影响,进而实现不经过杂波抑制而直接运动目标检测的目的。同时由于不使用训练样本,可很好地避免训练样本内的非均匀性,该算法在实际非均匀杂波场景中有更广泛的应用前景。     

对象监控的协同入侵检测

 分析现有入侵检测技术存在的问题,主要在于数据的获取及检测方法上。据此,提出了一种解决这些问题的新途径,就是紧紧抓住入侵检测的本质问题,从对象分类入手,给出了一个协同入侵检测模型,描述了该模型的各个部件。设计了入侵事件检测代理及融合中心,重点阐述了如何应用场景与自动机技术,将检测一个入侵的方法扩展到检测一类入侵的问题,对可序列化的攻击,文章提出的检测方法能检测其变种攻击。实验检验了方法的有效性。