知识网络中的社会资本及其作用

知识网络的绩效,在一定程度上取决于网络伙伴间社会资本与信任关系的形成。社会资本在知识网络中起着不可忽视的重要作用,其积极作用主要有:促进组织间交互学习;减少交易费用;促进风险分担;形成知识网络的竞争优势,进而增强网络成员的竞争优势;有利于解决知识网络中产生的"囚徒困境"等问题。而其消极作用主要有:易帮派化;易冒进;易产生锁定。     

International market selection and subsidiary performance: A neural network approach

How should multinational enterprises (MNEs) select international markets? We develop a model of international market selection that adds firm-specific advantages and transaction cost considerations to previously explored target market factors based on Dunning's Eclectic Framework. Results obtained using neural network (NN) analysis indicates that our model has strong predictive power in explaining international market selection. Further tests show that firms selecting international markets predicted by the model reported significantly higher subsidiary performance relative to firms whose investments were not predicted by the model. Our results provide strong initial evidence that a firm-level strategic approach to international market selection facilitates MNE success.     

Migrant networks, language learning and tourism employment

This paper examines the relationship between migrants’ social networks, the processes of language acquisition and tourism employment. Data collected using netnography and interviews are used to identify the strategies that Polish workers in the UK use to develop their language skills. The paper highlights the roles played by co-workers, co-nationals and customers in migrants’ language learning, both in the physical spaces of work and the virtual spaces of internet forums. It also shows how migrant workers exchange knowledge about the use of English during different stages of their migration careers: prior to leaving their country of origin and getting a job, during their employment and after leaving their job. Implications for academic inquiry and human resource management practice are outlined.     

Networking responses to different levels of institutional void: A comparison of internationalizing SMEs in Egypt and the UK

This paper reports a comparative qualitative study of how decision-makers in internationalizing SMEs respond to relevant institutions in their domestic environment through networking activity. Twenty SMEs are compared respectively from a developing economy (Egypt) and a developed economy (UK). The two countries contrast both in the effectiveness of their institutional support for SMEs and in their cultural norms towards network relationships. Substantial differences are found between the two national samples in SME decision makers’ networking behaviour in response to specific institutional conditions. The links between institutional conditions, national cultural norms and SME networking responses are explicated in a new theoretical model.     

多层级视角下京津冀科技创新政策府际关系研究

府际关系协调有利于实现创新资源、要素的有效汇聚与整合,进而提升政策有效性。以京津冀为例,构建中央-地方多层级府际关系模型,分析中央政策主体合作网络特征与政策扩散特征、地方政策主体合作网络特征与政策执行特征的相关性。研究发现,中央政府部门间形成了以科技部为核心的核心—边缘网络结构特征;北京市政府部门间呈现出以中关村科技管理委员会为核心的核心-边缘网络结构特征;天津市政府部门间呈现出以天津市科学技术委员会为核心的星型网络结构特征;河北省则没有形成稳定的网络关系。中央政策主体合作网络中的度中心性与政策扩散广度具有相关性;地方政策主体合作网络中介中心性、度中心性与政策执行强度具有相关性。     

论我国中小企业技术创新网络的建设

大量研究表明中小企业的发展需要一个完善的技术创新网络,所以建立完善的技术创新网络对我国中小企业的发展是至关重要的。文章分析了技术创新网络的概念及特征,分析了技术创新网络对中小企业发展的作用,指出了我国目前中小企业技术创新网络建设存在的问题,最后,就如何培育技术创新网络,促进我国中小企业发展提出了建议。     

Cross-border cooperation between securities regulators

The events of September 11, 2001, prompted sweeping cross-border coordination efforts for securities regulators around the globe. After 9/11, the International Organization of Securities Commissions (IOSCO) forged a nonbinding arrangement—the Multilateral Memorandum of Understanding Concerning Consultation and Cooperation and the Exchange of Information (MMoU)—that standardized the protocol for information sharing among participating securities regulators. Because regulators from different countries entered the MMoU at different times, their enlistments created a set of staggered shocks. I use these shocks to show that the resulting cross-border cooperation (a) increases cross-border enforcement and (b) reduces the cost of liquidity provision in the capital markets of participating countries. These results support the conclusion that the MMoU helps fill gaps in cross-border regulation that historically exposed investors to information asymmetry, agency costs, and expropriation risks.     

Using Artificial Neural Networks to forecast Exchange Rate,including VAR‐VECM residual analysis and prediction linear combination

The Euro US Dollar rate is one of the most important exchange rates in the world, making the analysis of its behavior fundamental for the global economy and for different decision‐makers at both the public and private level. Furthermore, given the market efficiency of the EUR/USD exchange rate, being able to predict the rate's future short‐term variation represents a great challenge. This study proposes a new framework to improve the forecasting accuracy of EUR/USD exchange rate returns through the use of an Artificial Neural Network (ANN) together with a Vector Auto Regressive (VAR) model, Vector Error Corrective model (VECM), and post‐processing. The motivation lies in the integration of different approaches, which should improve the ability to forecast regarding each separate model. This is especially true given that Artificial Neural Networks are capable of capturing the short and long‐term non‐linear components of a time series, which VECM and VAR models are unable to do. Post‐processing seeks to combine the best forecasts to make one that is better than its components. Model predictive capacity is compared according to the Root Mean Square Error (RMSE) as a loss function and its significance is analyzed using the Model Confidence Set. The results obtained show that the proposed framework outperforms the benchmark models, decreasing the RMSE of the best econometric model by 32.5% and by 19.3% the best hybrid. Thus, it is determined that forecast post‐processing increases forecasting accuracy.     

Can machine learning approaches predict corporate bankruptcy? Evidence from a qualitative experimental design

Bankruptcy prediction has received a growing interest in corporate finance and risk management recently. Although numerous studies in the literature have dealt with various statistical and artificial intelligence classifiers, their performance in credit risk forecasting needs to be further scrutinized compared to other methods. In the spirit of Chen, Härdle and Moro (2011, Quantitative Finance), we design an empirical study to assess the effectiveness of various machine learning topologies trained with big data approaches and qualitative, rather than quantitative, information as input variables. The experimental results from a ten-fold cross-validation methodology demonstrate that a generalized regression neural topology yields an accuracy measurement of 99.96%, a sensitivity measure of 99.91% and specificity of 100%. Indeed, this specific model outperformed multi-layer back-propagation networks, probabilistic neural networks, radial basis functions and regression trees, as well as other advanced classifiers. The utilization of advanced nonlinear classifiers based on big data methodologies and machine learning training generates outperforming results compared to traditional methods for bankruptcy forecasting and risk measurement.