On the Nonlinear Predictability of Stock Returns Using Financial and Economic Variables

In a recent article by Qi, neural networks trained by Bayesian regularization were used to predict excess returns on the S&P 500. The article concluded that the switching portfolio based on the recursive neural-network forecasts generates higher accumulated wealth with lower risks than that based on linear regression. Unfortunately, attempts to replicate the results were unsuccessful. Replicated results using the same software, approach and data detailed by Qi indicate that, in fact, the switching portfolio based on the recursive neural-network forecasts generates lower accumulated wealth with higher risks than that based on linear regression.     

How to make e-Procurement viable for SME suppliers

Due to their common role as suppliers, small and medium-sized enterprises (SMEs) are especially challenged by today's turbulent business conditions. In order to meet this challenge, short-term WWW-based enterprise cooperations are regarded as an appropriate way to enhance the competitive strength of SMEs. Hence, the Institute of Machine Tools and Industrial Management (IWB) operates three so-called competence networks for engineering (www.engineering-net.de), rapid prototyping (www.rp-net.de) and manufacturing (www.produktionsnetz.de) services. For the purpose of an applied research project, more than 80 participating SME suppliers have been included in these specialized virtual markets, which are based on the core competencies of the cooperating SMEs. Additionally, the platform (www.virtueller-markt.de) enables customers to configure their specific supply chains via the above-mentioned competence networks. The presented paper depicts how to establish and operate these competence networks as well as how to configure supply chains via these virtual markets.     

Duality of departmental specializations and PhD exchange: A Weberian analysis of status in interaction using multilevel exponential random graph models (mERGM)

Weber proposes that lifestyle similarities preserve status by producing interactional closure between status similar actors. I investigate this theory on academic status hierarchies by conceptualizing sub-disciplinary specializations as departmental lifestyles and PhD exchange networks as interdepartmental interactions. Multilevel exponential random graph models (mERGM) reveal that the more specializations departments share, the more likely they are to exchange personnel. On the flip side, departments that do not share specializations are less likely to exchange doctoral candidates. Moreover, shared specializations are key determinants of closure between elite departments. These results support Weber’s theory and suggest that shared specializations preserve existing patterns of inequality between elite and non-elite departments.     

基于贝叶斯正则化的TDBPNN模型在中国外贸预报中的应用及评估

基于相空间重构的非线性预报思想,建立一个时滞的BP神经网络模型(TDBPNN),采用贝叶斯正则化方法提高BP网络的泛化能力,并将该模型应用于中国进出口贸易的预测,结果证明改进的TDBPNN模型具有较好的泛化能力,准确拟合了进出口贸易发展的历史值和趋势。区别于一般的预测评价,认为非线性预测不仅要注重数据拟合和精度改进,而且应该能够反映被预报系统的非线性特征。在分析模型预测精度的同时,通过计算拟合序列和原序列的非线性特征量进行模型评价,证实预测模型能够合理地“捕捉”到产生原序列的非线性系统的动力学特征。     

On Shortest k-Edge-Connected Steiner Networks in Metric Spaces

Given a set of points P in a metric space, let l(P) denote the ratio of lengths between the shortest k-edge-connected Steiner network and the shortest k-edge-connected spanning network on P, and let r = inf l(P) P for k 1. In this paper, we show that in any metric space, r 3/4 for k 2, and there exists a polynomial-time -approximation for the shortest k-edge-connected Steiner network, where = 2 for even k and = 2 + 4/(3k) for odd k. In the Euclidean plane, and .     

Synergy from configuration of global production networks: drivers,mechanisms, and outcomes

Abstract

This paper explores the network synergies available from the configuration of highly dispersed, global production networks (GPNs). Synergy theory from the literature on intra-firm and inter-firm GPNs is examined in terms of drivers, mechanisms and outcomes. An explanatory model is developed and enriched via a series of extended case studies of firms undertaking re-configuration of their GPNs during the period 2003–2014. The primary finding is that firms create synergy in their GPNs by selecting specific configuration mechanisms, in response to a certain set of external drivers, in order to achieve certain synergistic outcomes. The paper classifies the main types of drivers, mechanisms and outcomes in terms of key variables and discusses their inter-connections and relationships.     

Understanding the emergence of redistributed manufacturing: an ambidexterity perspective

Abstract

This paper extends operations strategy theory on efficiency and flexibility trade-offs to the emergent phenomenon of redistributed manufacturing (RDM). The study adopts a multiple-case design including five small and five large pharmaceutical firms. We propose that organizations can gain the efficiency benefits of centralized manufacturing and the flexibility advantages of RDM by building an ambidexterity capability. To build such a capability, large firms can structurally partition their manufacturing and supply management functions, with one sub-unit managing centralized production and the other RDM. Smaller enterprises can build an ambidexterity capability by creating the right organizational context, where multi-skilled workers switch between efficient and flexible tasks. This paper contributes to theory by explaining the emergence of RDM using an organizational ambidexterity lens, laying the groundwork for new theory development in the field. We provide managers with a practical example of how to build an ambidexterity capability to realize flexibility and efficiency advantages.     

网络组织的机制、演化与形态研究

本文以人们对生产组织制度的认识过程为线索,围绕网络组织的运行机制、形成过程以及多种多样的表现形态,探讨了网络组织的理论认知和实践活动.在与企业内部的强制调节、市场组织的自动调节进行比较后,作者论证了网络组织是一种比市场更有效、比企业更灵活的组织生产活动的方式.     

神经网络在企业流动资金需求量预测中的应用

文章在对流动资金需求量预测的传统方法进行分析的基础上 ,指出了神经网络方法的适用性。在技术分析的基础上 ,建立了神经网络的预测模型 ,并进行了实证分析     

A comparison of Japanese failure models: Hybrid neural networks,logit models,and discriminant analysis

This article looks at the ability of a relatively new technique, hybrid artificial neural networks (ANNs), to predict Japanese banking and firm failures. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting failure for one year prior to the event. This suggests that for researchers, policymakers, and others interested in early warning systems, the hybrid network may be a useful tool for predicting banking and firm failures.