知识型员工双重社会网络对企业创新绩效作用机制研究

基于企业基础资源观和组织学习理论，从知识型员工个人和组织社会网络两个方面构建知识型员工双重社会网络影响企业创新绩效的理论模型，分析知识共享、组织学习及资源整合在员工双重社会网络对企业创新绩效影响机制中的作用。结果表明：知识型员工双重社会网络对科技型企业创新绩效的作用路径有3条，资源获取与整合、知识共享与学习及员工动态创新能力分别在其中发挥中介作用；在不同类型组织文化环境中，知识型员工双重社会网络对企业创新绩效的作用特征、作用重点以及作用机制存在显著差异，内部整合维度主要通过知识共享和组织学习影响企业创新绩效，外部适应维度主要通过隐性知识传播和资源整合影响企业创新绩效。     

智能生产与服务网络下新型产业创新平台运行模式研究

在智能生产与服务网络化背景下，传统产业创新平台技术含量低、创新积极性不高、持续升级能力弱、成果转化效率低等矛盾日益凸显。针对该问题，提出了智能生产与服务网络下的新型产业创新平台运行模式。首先从科技支撑、智能生产与服务组织、感知与信息传输、基础设施和平台规制角度研究其组织结构，并从个性化定制、模块化集成和全程价值链供给角度研究其功能结构；然后从协同创新、导向、风险和产业生态进化角度研究其保障机制；最后从市场化角度研究其运行路径。通过研究智能生产与服务网络下的新型产业创新平台运行模式，为实施传统产业高端化、推动行业进步、促进经济发展提供参考与借鉴。     

基于冠层光谱和BP神经网络的水稻叶片氮素浓度估算模型

[目的]快速、准确地诊断水稻叶片氮素营养状况,为水稻氮肥精准管理提供依据。[方法]以江西省农科院8种不同施肥处理的晚稻为研究对象,于主要生育期同步测定了水稻冠层光谱反射率及叶片全氮浓度(Leaf Nitrogen Concentration,LNC),系统分析了原始光谱反射率、一阶微分光谱、"三边"参数以及由350～1 350nm两两波段组合的差值(SD (Rλ1,Rλ2))、比值(SR (Rλ1,Rλ2))及归一化(ND(Rλ1,Rλ2))光谱指数与水稻LNC的相关关系,筛选出敏感参数,并以之为自变量构建了水稻LNC的传统预测模型,另外构建不同指标个数的多元线性与BP神经网络模型,并对模型进行验证。[结果](1)水稻LNC与一阶微分光谱在751nm处的相关性最高(r=0. 822);(2)"三边"参数中的红边面积SDr与LNC的相关性较高(r=0. 687);(3) 750nm附近的红边波段与近红外波段差值组合、550nm附近的绿光波段与近红外波段的比值及归一化差值组合与水稻LNC的相关性较高,以SD (R752,R751)、SR (R534,R1 350)和ND (R534,R1 349)表现最好,相关系数分别为0. 827、-0. 790和0. 788;(4)传统回归模型中以SD(R752,R751)构建的一元线性模型最佳(RC2=0. 665、RV2=0. 750、RMSEV=0. 4%、RPD=2. 034);(5)利用5个指标((R'751、SDr、SD (R752,R751)、SR (R534,R1 350)、ND (R534,R1 349))经逐步回归筛选出的2个指标SD ((R752,R751)和SR (R534,R1 350))构建预测水稻氮素的BP神经网络模型,预测效果最佳,其验证参数值分别为R2=0. 859、RMSEV=0. 302%和RPD=2. 669。[结论]基于单指标构建的传统线性模型计算过程简单但精度略低,而基于2个指标(SD (R752,R751)、SR (R534,R1 350))构建的BP神经网络模型预测精度高于该2指标构建的多元线性模型,表明在指标适合的情况下,BP神经网络对氮素具有较好的预测能力,是一种快速准确估算水稻叶片全氮浓度的方法。     

Network structure,portfolio diversification and systemic risk

We investigate the effect of portfolio diversification on banking systemic risk, where the network effect is incorporated. We analyze three kinds of interbank networks, namely, random networks, small-world networks and scale-free networks. We show that the effect of portfolio diversification on banking systemic risk depends on interbank network structures and shock types. First, systemic risk increases first and then reduces with the increase of the level of portfolio diversification in the case of the individual shock. Second, in the case of the systemic shock, systemic risk reduces with the increases of the level of portfolio diversification. Third, banking systems with scale-free network structures are the most stable, and those with small-world network structures are the most vulnerable.     

中国金融系统风险溢出效应研究——基于溢出指数和波动溢出网络

文章基于溢出指数和波动溢出网络方法，从静态和动态分别度量我国金融系统不同子市场间的风险联动水平及变动趋势。研究结果表明，我国金融系统风险溢出效应整体水平较高，各市场间联动性较强；市场内部滞后效应大于市场之间溢出效应，两两有向溢出效应具有强不对称性；作为我国金融系统的风险中心，货币市场始终处于风险溢出方，但在金融危机时期，其对外风险溢出效应相对减弱，而大宗商品市场、资本市场、房地产市场等其余市场的风险溢出效应相对增强。值得注意的是，当前我国金融系统整体风险溢出水平较高，市场间风险相关性较强，需要进一步加强系统性风险的监测与防范。     

小世界网络视角下外向型开放式创新输出机制建模及仿真研究

现有关于网络环境下开放式创新的研究大都聚焦于内向型开放式创新，对于外向型开放式创新的关注不足。为更加真实地展现创新网络中知识创新、创新输出的整体行为机制，基于小世界网络仿真建模方法，从创新输出企业视角，探究创新输出与创新网络、创新绩效之间的内在关系。结果发现：网络关系强度、创新开放度与网络整体知识水平呈正相关关系，而个体网络规模与网络整体知识水平呈非线性相关关系；网络关系强度与网络知识水平分布均匀性呈正相关关系，创新开放度、个体网络规模与网络知识水平分布均匀性均呈非线性相关关系；当企业创新能力较强、解吸能力较弱时，网络整体知识水平和网络知识分布差异较大。创新输出企业可以结合网络结构和网络关系，优化创新网络、主体能力与创新开放度，制定开放策略，从而实现获取创新收益与保持自身竞争优势的双重平衡。     

A study on the deployment of high-speed broadband networks in NUTS3 regions within the framework of digital agenda for Europe

This paper investigates the high-speed broadband situation in the EU and its prospects. It uses a deployment model to estimate the investment required to meet the Digital Agenda for Europe (DAE) broadband targets set by the European Commission in its Europe 2020 strategy at different stages: as of 2016, after expected operators’ deployment, after public subsidies and leveraged investment, and as expected in 2020. The model uses data at the NUTS3 level, which is the most granular level that has data available on the status of broadband deployment, to arrive at a coherent and comparable framework. From the different perspectives on the investment to meet DAE targets, the paper concludes on the need for an appropriate combination of incumbent and alternative operators investments, public subsidies and leveraged investments, and new investments, both public and private and non-existing as of 2016, examining their feasibility and the impact of different regulatory, technical, and policy strategies.     

Modelling the iron ore price index: A new perspective from a hybrid data reconstructed EEMD-GORU model

As iron ore is the fundamental steel production resource, predicting its price is strategically important for risk management at related enterprises and projects. Based on a signal decomposition technology and an artificial neural network, this paper proposes a hybrid EEMD-GORU model and a novel data reconstruction method to explore the price risk and fluctuation correlations between China’s iron ore futures and spot markets, and to forecast the price index series of China’s and international iron ore spot markets from the futures market. The analysis found that the iron ore futures market in China better reflected the price fluctuations and risk factors in the imported and international iron ore spot markets. However, the forward price in China’s iron ore futures market was unable to adequately reflect the changes in the domestic iron ore market, and was therefore unable to fully disseminate domestic iron ore market information. The proposed model was found to provide better market risk perceptions and predictions through its combinations of the different volatility information in futures and spot markets. The results are valuable references for the early-warning and management of the related enterprise project risks.     

基于人工神经网络的互联网公司估值方法研究

资本市场认为互联网公司市值的驱动因素应包括盈利因子、运营因子、流量因子和协同因子。将协同效应指标考虑到公司估值体系中,意图构造互联网公司优化估值模型。使用美股上市的互联网企业数据建立了评价指标体系,通过因子分析实现了二级指标降维,通过实证分析确认了四个因子与公司市值的相关关系,最后构建了基于人工神经网络BP算法的互联网公司估值模型,通过预测数据的检验发现模型的准确度较高。随着2018年互联网公司美股上市潮的持续,该模型能有效为资本市场估值提供参考。     

Investigating tail-risk dependence in the cryptocurrency markets: A LASSO quantile regression approach

We construct the complete network of tail risk spillovers among major cryptocurrencies using the Least Absolute Shrinkage and Selection Operator (LASSO) quantile regression. We capture important features of the network, including major risk-driving and major risk-receiving currencies, and the evolution of the tail dependence among the currencies over time. Importantly, we reveal a striking finding that the right tail dependence among the cryptocurrencies is significantly stronger than the left tail counterpart. This unique characteristic may have contributed to the rise in popularity of cryptocurrencies over the last few years. Our portfolio analysis reveals that diversification in cryptocurrency investment can be accomplished simply by employing the naïve equal-weighted scheme even when transaction costs are taken into account.