基于神经网络系统的项目投资风险评价探讨

与传统的评价方法不同，神经网络模型具有自学习、自组织、自适应能力等的特点，它较好的克服了传统项目评价局限性以及依赖专家经验的弊端，为项目投资风险评价开辟了新途径。论文阐述了神经网络结构与原理，分析了项目投资风险评价的神经网络模型，通过应用评价，进一步探讨其应用价值及其意义。     

基于企业基因重组理论的价值网络构建研究

全球化、信息化带来市场交易成本的急剧下降，并直接导致组织创造价值的方式发生了变化。本文借鉴企业基因和企业基因组的概念，对企业基因重组理论进行了系统地完善和整合。依此理论为基础，本文对能力要素驱动型价值网络的形成、特征和构建策略进行了深入地分析。     

Developing a Community of Practice: Michael Gaffikin and Critical Accounting Research

This paper demonstrates the role of a community of practice in academic endeavour, focusing on the influence of place and the role of thought leaders in guiding academic development. This is illustrated with reference to the influence of Emeritus Professor Michael Gaffikin in establishing a critical accounting community of practice at the University of Wollongong (UOW) through his PhD supervisions. Social network analysis (SNA) is used to visualize the 43 PhD supervisions undertaken by Gaffikin during his career, and subsequent PhD supervisions of his students, and students of those students. SNA illustrates the structure of relationships, and the paths through which scholars learnt from one another, which we combine with qualitative analysis of recollections, acknowledgments, and doctoral theses. We demonstrate the role of Gaffikin, as the intellectual thought leader, and UOW, as the intellectual place, in the development of the critical accounting community of practice. The development of critical accounting scholarship was a function of Gaffikin's intellectual and professional leadership, which he executed through PhD supervision, the annual Doctoral Consortium, and his direction at UOW. This paper highlights the importance of local communities for the development of research agendas, and the influence of PhD supervisors on the professional development of students.     

Managing and financing metropolitan public services in China: experience of the Pearl River Delta region

Under China’s current fiscal policies and inter-governmental relations, it is a significant challenge to finance and deliver public services across jurisdictions. This challenge was met in the Pearl River Delta region in southern China with a collaborative governance approach. Directives from higher-level governments and horizontal inter-city fiscal arrangements were successfully combined to deliver public services. Effective networks should be developed to improve co-ordination and collaboration in delivering cross-jurisdictional public services.     

Learning minimum variance discrete hedging directly from the market

Option hedging is a critical risk management problem in finance. In the Black–Scholes model, it has been recognized that computing a hedging position from the sensitivity of the calibrated model option value function is inadequate in minimizing variance of the option hedge risk, as it fails to capture the model parameter dependence on the underlying price (see e.g. Coleman et al., J. Risk, 2001, 5(6), 63–89; Hull and White, J. Bank. Finance, 2017, 82, 180–190). In this paper, we demonstrate that this issue can exist generally when determining hedging position from the sensitivity of the option function, either calibrated from a parametric model from current option prices or estimated nonparametricaly from historical option prices. Consequently, the sensitivity of the estimated model option function typically does not minimize variance of the hedge risk, even instantaneously. We propose a data-driven approach to directly learn a hedging function from the market data by minimizing variance of the local hedge risk. Using the S&P 500 index daily option data for more than a decade ending in August 2015, we show that the proposed method outperforms the parametric minimum variance hedging method proposed in Hull and White [J. Bank. Finance, 2017, 82, 180–190], as well as minimum variance hedging corrective techniques based on stochastic volatility or local volatility models. Furthermore, we show that the proposed approach achieves significant gain over the implied BS delta hedging for weekly and monthly hedging.     

On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks

Daily and weekly seasonalities are always taken into account in day-ahead electricity price forecasting, but the long-term seasonal component has long been believed to add unnecessary complexity, and hence, most studies have ignored it. The recent introduction of the Seasonal Component AutoRegressive (SCAR) modeling framework has changed this viewpoint. However, this framework is based on linear models estimated using ordinary least squares. This paper shows that considering non-linear autoregressive (NARX) neural network-type models with the same inputs as the corresponding SCAR-type models can lead to yet better performances. While individual Seasonal Component Artificial Neural Network (SCANN) models are generally worse than the corresponding SCAR-type structures, we provide empirical evidence that committee machines of SCANN networks can outperform the latter significantly.     

计算机Linux网络操作系统特点及服务器配置

Linux系统作为一款网络操作系统,它具有很好的网络操作功能,网络操作系统(NOS)是网络的心脏和灵魂,是向网络计算机提供服务的特殊的操作系统。     

企业如何利用社会网络提升吸收能力

在知识经济的背景下,吸收能力被认为是企业提升绩效和增强创新的战略能力.社会网络被认为可以对潜在的吸收能力和实现的吸收能力都产生积极影响.本文从社会网络的关系层面,从外部和内部两个视角,分别阐述了社会网络如何影响吸收能力的两个维度.本文还从提升吸能力的视角,提出了有效构建和维护社会网络的措施.     

“前置式过程交叉型”网络化论文指导模式研究——以上海市某高校工商管理专业自考生论文指导为例

网络化论文指导模式是信息化时代成人教育中论文指导的趋势。如何发挥网络指导论文写作模式的优势,避免其劣势,是这一模式需要解决的问题。针对该模式目前存在问题,提出了前置式的论文质量控制模式。     

Information transmission between large shareholders and stock volatility

Does information transmission between large shareholders exacerbate stock volatility or reduce stock volatility? In this study, based on the common shareholding data in the Chinese stock market from 2007 to 2017, we establish the shareholder’s coshareholding network, and use the weighted network clustering coefficient to measure the information transmission speed. Moreover, we investigate the impact of information transmission speed on stock volatility. The empirical results show that a nonlinear U-shaped relationship exists between information transmission speed and stock volatility. The findings are robust to the inclusion of other determinants of stock volatility identified in the literature. Finally, we verify the existence of private information interaction in the coshareholding network.