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.     

基于转移概率和网络联系的辽宁省农村居民点适宜性评价

[目的]农村居民点适宜性评价在农村居民点调控、规划和预测中具有基础作用,且农村居民点适宜性评价应顾及农村居民点之间的社会经济活动联系。[方法]以辽宁省为研究区,首先运用逐步Logistic回归模型构建农村居民点转移概率,其次运用网络分析方法确定农村居民点图斑间的网络联系强度,最终通过加权求和方法确定农村居民点转化概率,进行农村居民点空间布局适宜性评价。[结果]辽宁省农村居民点可以划分为高度适宜类、较适宜类、一般适宜类和不适宜类4类,分别占农村居民点总面积的比例为28.18%、31.32%、13.26%和27.24%;从各地貌分区来看,高度适宜类主要集中分布在中部平原地区和东部山地丘陵区中的南方沿海地区,而不适宜类整体分布则较为零散;辽宁省中部平原区位条件好,东部沿海地区区位条件也较好,西部山地丘陵区地区较差。[结论]研究方法和结论有助于全面把握农村居民点社会经济与区位适宜性特征,为农村居民点科学规划提供科学指导。     

基于电子商务平台的农产品营销模式研究——以重庆为例

随着互联网普及程度越来越高,农产品电子商务作为一种新型营销方式在现代营销中具有重要的作用。文章首先对电子商务相关概念和基础理论进行概述,明确了电子商务的重要性。其次在对重庆市农产品电子商务营销现状及问题分析的基础上,对目前重庆市现有的3种电子商务营销模式,即为电子市场交易模式、网络中介模式、网络虚拟社区模式进行深入分析。最后,从建立健全农产品电子商务相关法律和制度、大力培养农产品电子商务专业人才、开展多种形式的农产品电子营销模式,实施农牧产品与农商旅联动营销战略等几方面提出促进重庆市电子商务发展的策略。     

基于深度学习模型的种植结构复杂区农作物精细分类研究

目的 卫星遥感技术具有覆盖范围广、探测周期短、调查成本低等优势而广泛应用于大区域农作物分类。然而在种植结构复杂区（如城乡结合部）,因其地块破碎、同期生长的作物种类多且分布分散,利用传统的统计分类或机器学习方法进行农作物分类时仍存在精度不高的问题。为提高种植结构复杂区农作物分类精度。方法 文章选取河北省廊坊市广阳区为研究区,以GF-1 PMS全色多光谱融合影像为数据源,采用U-Net、PSPNet及DeepLabv3+,3种深度学习模型进行农作物分类研究。分析模型参数对农作物分类精度的影响,评价3种深度学习模型的农作物分类精度,优选农作物精细分类方法。结果 （1）学习率与3种深度学习模型的分类精度呈正相关关系,较大的学习率（0.01,0.001）下,3种模型收敛速度快,分类精度高。批样本量与模型分类稳定性相关,批样本量设为100时,3种模型的分类稳定性最好。（2）相比PSPNet、DeepLabv3+模型,U-Net模型分类效果最好,总体分类精度为89.32%。（3）GF-1 PMS影像结合U-Net模型可有效提升种植结构复杂区农作物分类精度,大宗作物春玉米、夏玉米的分类精度在80%以上,花生、红薯、蔬菜小宗作物分类精度在60%以上。结论 该研究可为准确获取种植结构复杂区的农作物类型、面积及空间分布信息提供参考依据。     

用PERT法求解不确定网络中活动时间参数的改进研究

基于计划评审技术(PERT)中β分布的假设,阐述了常规三时估计方法的不足,提出了相应解决对策,即采用限定概率三时估计法分别建立了β分布和Gamma分布拟合方差最小模型,用微粒群算法求解模型,并且将求解结果与常规三时估计方法进行了对比研究。实例证实,对于不同类型的活动,可以选择合理的分布类型并利用限定概率三时估计法,对各个活动的持续时间服从的分布密度函数进行拟合,从而提高PERT的精度和效率。     

航线网络布局模式研究

航线网络布局问题是民航运输业的首要决策问题,布局合理的航线网络是提高航空公司竞争力的有效途径,对推动民航运输生产发展具有重要的战略意义。本文在对城市对航线网络和枢纽航线网络进行分析的基础上,运用比较分析的方法对两种航线网络进行了经济性研究,详细探讨了两种航线网络的优缺点,并且结合目前民航现状,对我国的航线网络布局模式进行了探索。     

基于Elman神经网络的商业银行信贷风险识别及评估模型

建立了基于Elman神经网络的商业银行信贷风险识别及评估模型,并通过实例验证了模型的准确性和可靠性。研究过程及结果表明,基于Elman神经网络的商业银行信贷风险识别及评估模型能够很好地反映信贷过程中的非线性因素,准确地预测出完整的信贷风险评估指标和信用等级之间的映射关系,能快速评估和有效减低商业的信贷风险。一组实例结果显示该评估模型的准确率接近90％。