基于差异信息量的多源数据融合方法

D-S证据理论可应用于多源数据融合领域，但在处理高度冲突的证据时，可能会出现反直觉的结果.为解决这一问题，本文提出了差异信息量的概念及融合方法.首先，通过信息熵表明证据的相对重要性，采用散度获取证据可信度.然后利用证据可信度优化证据差异度以得到差异信息量，经过计算获取数据的最终权重，并将其作为D-S证据理论中的基本概率分配进行决策.在处理冲突证据、一致证据及不同数量证据等方面的数据融合问题时与其他方法对比，所提方法收敛更快，准确度更高.故障诊断的应用实例表明，所提方法的不确定性更小，优于现存的其他方法.     

基于多参数稳定分布的GBAS垂直保护级计算

针对全球导航卫星系统(global navigation satellite system, GNSS)的地基增强系统(ground based augmentation system, GBAS)的完好性评估过程中, 使用误差包络理论计算保护级(protection level, PL)时, 误差模型存在的“厚尾”“不对称”“非零均值”等问题, 提出了基于稳定分布的误差包络理论。首先给出了高斯包络理论计算PL的模型; 其次推导了两参数与四参数稳定分布误差包络算法; 最后使用仿真数据和通过实验测得的数据, 对多参数稳定包络模型的算法进行评估。结果表明多参数稳定包络法在相似的计算复杂度下, 解决了高斯包络法建模时过于保守的问题, 显著提高了保护级计算的精确性。     

Moving beyond Volatility Index (VIX): HARnessing the term structure of implied volatility

This paper considers how information from the implied volatility (IV) term structure can be harnessed to improve stock return volatility forecasting within the state-of-the-art HAR model. Factors are extracted from the IV term structure and included as exogenous variables in the HAR framework. We found that including slope and curvature factors leads to significant forecast improvements over the HAR benchmark at a range of forecast horizons, compared with the standard HAR model and HAR model with VIX as IV information set.     

一种改进的基于人体静电冲击模型应力的瞬态功率模型

提出一种改进的基于人体静电冲击模型(Human Body Model, HBM)应力的瞬态功率模型。利用HSPICE仿真软件, 模拟MOS管遭受的HBM应力, 得到对应的等效直流电压。HBM电路的预充电电压与MOS管对应的等效直流电压值的散点图表明, 两者保持线性关系, 并通过拉普拉斯变化得到证明。与现有的瞬态功率模型相比, 改进后的模型降低了在HBM应力作用下的计算复杂度, 可以更加简便地从统计学上预测MOS管栅氧击穿的发生, 给HBM冲击作用下MOS管栅氧化层可靠性的评估提供参考。     

合作创新的领导权博弈与利益协调研究

研究了合作创新的权力结构对知识创造及创新绩效的作用机理.考虑上下游企业合作创新的知识共享、创造及利益分配过程,界定权力均衡(LS)、上游企业领导(UL)、下游企业领导(DL)等领导权博弈模型并进行创新效率分析.研究发现:权力结构影响合作创新效率,LS模式下企业的知识产出与供应链利润优于UL、DL模式;企业的知识溢出既可提高合作伙伴的知识产出与创新利润,也存在知识外溢损失风险,UL、DL模式下领导者(跟随者)知识溢出与其创新利润正(负)相关,LS模式下企业的知识溢出与其创新利润呈"倒U型"关系;创新贡献率是引起合作利益冲突的主要原因,使用Nash协商模型检验了通过协商机制解决利益协调问题的有效性,比较发现LS模式的利益协调效果优于UL、DL模式.最后,讨论了研究的管理启示.     

贫困村农户电商扶贫参与意愿及其影响因素研究——以资源枯竭型城市阜新为例

基于阜新市贫困村407份调查数据,识别了农户参与电商扶贫的意愿,并运用多元有序Logit模型实证分析了农户参与意愿的影响因素。研究表明,农户对电商扶贫的参与意愿较高;受教育程度、网络使用经验、主要经济来源、农村电商认知、电商扶贫收入效应认知、亲戚朋友电商扶贫参与意愿、政府电商扶贫相关工作、电商服务站相关工作、电商扶贫服务体系、特色农产品规模对农户电商扶贫参与意愿有显著正向影响,家庭年均纯收入、生活满意度认知因素对农户电商扶贫参与意愿有显著负向影响。     

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.     

Google Trends and the forecasting performance of exchange rate models

In this paper, we use Google Trends data for exchange rate forecasting in the context of a broad literature review that ties the exchange rate movements with macroeconomic fundamentals. The sample covers 11 OECD countries’ exchange rates for the period from January 2004 to June 2014. In out‐of‐sample forecasting of monthly returns on exchange rates, our findings indicate that the Google Trends search query data do a better job than the structural models in predicting the true direction of changes in nominal exchange rates. We also observed that Google Trends‐based forecasts are better at picking up the direction of the changes in the monthly nominal exchange rates after the Great Recession era (2008–2009). Based on the Clark and West inference procedure of equal predictive accuracy testing, we found that the relative performance of Google Trends‐based exchange rate predictions against the null of a random walk model is no worse than the purchasing power parity model. On the other hand, although the monetary model fundamentals could beat the random walk null only in one out of 11 currency pairs, with Google Trends predictors we found evidence of better performance for five currency pairs. We believe that these findings necessitate further research in this area to investigate the extravalue one can get from Google search query data.     

机构投资者持股持续性对我国上市公司业绩及风险的影响研究

通过构建机构持股持续性指标体系,从四个维度全面研究机构持股持续性对上市公司业绩及风险的影响.整体来看,机构持股比例和持股稳定性的提升显著提高了公司业绩并降低了风险,反之则相反;机构持股期限与公司业绩仅呈很弱的显著负相关,导致这一异象的原因是:机构持股期限仅是一种表象,其背后隐含的是机构频繁换股的"散户化"行为及其不同的利益诉求带来了摩擦,加剧了公司经营决策和管理的不确定性,使公司业绩下降.分组研究显示:机构持股对公司业绩及风险的影响存在异质性;机构投资者只有秉持长期价值投资才利于公司业绩的提升,而以短期投机为目的的机构投资者对公司发展有害无益;机构持股稳定性越高,越有利于降低公司业绩风险;在机构持股比例高且持股期限长的公司中,机构持股比例与期限的协同效应越强,对公司业绩的改善作用越大,反之越小;在机构低比例持股的公司中,机构持股稳定性的提升对降低公司业绩风险的作用大于机构高比例持股的公司中对应的作用.     

轧制道次间插入冷却对特厚钢板组织与性能的影响

使用实验轧机旁冷却装置配合轧机进行轧制实验，研究轧制道次间不同冷却工艺对特厚钢板组织和性能的影响规律.研究结果表明:采用道次间冷却工艺可以在全厚度方向获得组织细化及强韧性提高效果，采用强冷道次间冷却实验钢1/4处晶粒尺寸可细化至10μm，强度为376MPa，-40℃冲击功为169J;心部晶粒尺寸可细化至15μm，强度为360MPa，-40℃冲击功为123J.本工艺可形成470μm厚表层细晶层，晶粒尺寸可细化至5μm;粗轧道次间插入冷却工艺轧制钢板强度和冲击韧性优于中间坯冷却工艺;随冷却强度增加，钢板内部组织明显细化且强度大幅提高.