制度差异、“一带一路”倡议与中国大型对外投资——基于投资边际、模式与成败的三重视角

本文基于2005—2016年中国609个企业2 440个大型对外投资微观样本，全面考察正式与非正式制度差异对企业投资边际、模式与成败的影响。研究发现：正式制度差异对投资规模化的影响不大，但显著影响投资多元化。企业为了避免正式制度差异带来的管理风险，倾向于选择全资股权结构，但企业选择绿地投资还是跨国并购并不受到正式制度差异的影响。相比之下，非正式制度差异的影响更为广泛。无论是正式制度差异还是非正式制度差异，均会显著增加企业投资的失败概率。进一步研究发现，“一带一路”倡议主要对正式制度差异存在优化互补效应。研究结论表明，夯实“一带一路”倡议，尤其大力发展与沿线国家的文化深度交流，有助于企业更快、更好地走出去。     

The impact on stock prices of deferral and elimination of internal control audit requirement for small firms

This paper examines the cumulative market reaction to the events related to deferral of internal control audit requirement under the Sarbanes-Oxley Act of 2002 and its elimination under the Dodd-Frank Act of 2010 for nonaccelerated filers (small firms). We document that small firms experienced negative cumulative abnormal returns around these events; and the differences between the cumulative abnormal returns for small firms and the two control groups (accelerated and large accelerated filers) were negative and significant at the 1% level. These results support the notion that market participants value the reliability of financial information irrespective of the firm size. Within the small firms, we find no firm characteristic significantly explains the market reaction to the events considered. That is, all small firms lost market value in reaction to the events that delayed and eliminated their internal control audit requirement.     

颠覆性技术识别与预测方法研究进展

为了把握颠覆性技术识别与预测方法研究进展，首先对颠覆性的内涵、特征和运行机制进行论述；然后，通过对已有文献进行系统梳理，将识别与预测方法分为5类，分别展开详述；最后，对各类方法进行比较分析。研究发现，已有颠覆性技术识别与预测方法仍处于发展的初期阶段，所有识别研究方法均围绕颠覆性技术的外部影响或内在特征中的某一方面展开，并未将二者有机结合起来。未来，应综合把握颠覆性技术内外部特征，构建全面、系统的识别与预测框架。     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

普惠金融与县域资金外流：减贫还是致贫*——基于中国592个国家级贫困县的研究

本文利用全国592个国家级贫困县的数据，采用空间计量模型实证分析了普惠金融对县域资金外流的影响，并验证了贫困县资金外流是否会产生致贫效应。本文研究发现：如果普惠金融只注重解决贫困地区对金融机构的接触性排斥，会进一步加剧资金外流，对减贫产生负向影响，即存在显著的致贫效应。这种致贫效应具有明显的空间外溢性，邻近县域的贫困状况在很大程度上会彼此“传染”，并具有空间衰减特征的地理边界。因而需要客观认识普惠金融的本质，有针对性地选择恰当有效的实施载体，解决好对信贷产品等关键金融服务的使用性排斥问题。     

产业融合对中国装备制造业创新效率的影响:结构嵌入的调节作用

从理论和实证两个方面探讨了产业融合对中国装备制造业创新效率的影响，以及网络中心性和网络异质性对二者关系的调节作用。基于2006—2015年省际装备制造7个子行业面板数据，采用ISCNFI指数测度模型和基于产出距离函数的超越对数前沿分析方法，分别测度装备制造业产业融合程度及创新效率变化情况，进而利用面板回归模型对理论假设进行实证检验。结果显示：产业融合程度与中国装备制造业创新效率存在倒“U”型曲线关系，即随着产业融合程度提升，中国装备制造业创新效率呈现出先上升后下降的变化趋势；网络中心性强化了产业融合对装备制造业创新效率的提升作用，而网络异质性削弱了产业融合对装备制造业创新效率的提升作用。     

Situating the Asian Infrastructure Investment Bank in the context of global economic governance

The creation of the Asian Infrastructure Investment Bank (AIIB)was welcomed by the World Bank but opposed by the Obamaadministration. The paper explains China’s positive relationshipwith the Bank and the Organization for Economic Cooperation andDevelopment (OECD) in terms of the mission of the Bank, sharedby the OECD, to develop and deepen the global economy. The AIIBand the related Belt and Road initiative promise to do this throughinvestment in infrastructure and connectivity in and around thepoorly integrated Eurasian landmass. But while the current Chineseleadership has supported an inclusive global economy based uponfree trade and supported by multilateral institutions, China’s controlof resources outside the multilateral framework and adherence topractices that challenge liberal principles prompt suspicions thatthese commitments are either disingenuous or anyway subjectto reversal. In itself, therefore, the AIIB provides no conclusiveevidence either way on China’s future course.     

Economic Impacts of the Possible China–US Trade War

This article uses a multi-country global general equilibrium (GE) model to numerically simulate the effects of possible China–US trade wars. We introduce an endogenous trade imbalance structure with trade cost into the model which helps to explore both tariff and non-tariff trade war effects. Our simulation results show that China will be significantly hurt by the China–US trade war, but negative impacts are affordable. The US can gain under unilateral sanction measures to China, but will lose if China takes retaliation measures. Comparing the effects under mutual trade war, China will lose more than the US. Introducing non-tariff barrier trade wars will intensify the negative effects, and comparatively negative effects to China are larger than to the US. Mexico’s involvement in trade war with the US will strengthen the negative effects and comparatively hurt the US more. Under non-cooperative and cooperative Nash bargaining equilibrium, the US can gain more than China in trade war negotiation, which means the US has stronger bargaining power than China. Additionally, trade wars between China and the US will hurt most countries and the world especially in GDP and manufacturing employment, but benefit their welfare and trade.     

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.     

Research engagement,impact and sustainable tourism

In this editorial, we reflect on how the Journal of Sustainable Tourism can contribute towards sustainable tourism researchers achieving more impact with their research. We propose some changes that can be tested in, and introduced gradually and collaboratively with, the community of the editorial board and authors. To support impactful mind sets, we will promote research that reflects diverse academic communities. To promote impactful research topics, we will encourage authors to frame their submitted articles against the Sustainable Development Goals, while research that is time sensitive will be fast tracked so it can contribute to current debates. To promote impactful methodologies, we shall favour articles that use mixed methods and action research, and those that conduct longitudinal, experimental, and evaluative research. To promote impactful partnerships, we will favour multidisciplinary approaches and research that has been co-created with stakeholders. To promote impactful communication and dissemination, we will continue to build an online community on social media for sustainable tourism researchers, we will promote articles in social media to raise their visibility, and we will provide free access to those articles that are deemed to have the greatest potential to impact positively on society.