Delegation and the regulation of U.S. financial markets

We analyze the institutional determinants of U.S. financial market regulation with a general model of the policy-making process in which legislators delegate authority to regulate financial risk at both the firm and systemic levels. The model explains changes in U.S. financial regulation leading up to the financial crisis. We test the predictions of the general model with a novel, comprehensive data set of financial regulatory laws enacted specifically between 1950 and 2009. The theoretical and empirical analysis finds that economic and political factors impact Congress’ decision to delegate regulatory authority to executive agencies, which in turn impacts the stringency of financial market regulation, and our estimation results indicate that political factors may have been stronger and resulted in inefficiencies.     

Revisiting the ICAPM under the distortion of risk–return tradeoff in short-horizon stock returns

We suggest that the distortion of the positive risk–return relation in the ICAPM is a consequence of trading by informed investors to exploit mispricing. We hypothesize and demonstrate that a non-positive (strongly positive) risk–return relation following positive (negative) market returns is attributed to short-selling (purchasing) of overpriced (underpriced) stocks along with optimistic (pessimistic) expectations conditional on good (bad) market news. We verify this asymmetry in the risk–return relation through the indirect risk–return relation conditional on good (bad) market news. We also find that the attenuation (reinforcement) of the positive risk–return relation is more profound in high- (low-) sentiment periods.     

Exchange rate dynamics and US dollar-denominated sovereign bond prices in emerging markets

Using data on Brazil, Colombia, Mexico, the Philippines, Russia and Turkey, our empirical results show that the exchange rates of their currencies have adequate explanatory power in explaining their US dollar-denominated sovereign bonds, particularly in the post-global financial crisis period. We develop a two-factor pricing model with closed-form solutions for the sovereign bonds in which the correlated factors are foreign exchange rates and US risk-free interest rates that follow a double square-root process relevant in the low interest rate environment. The numerical results and associated error analysis show that the model credit spreads can broadly track the market credit spreads.     

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。     

Do Cryptocurrencies Increase the Systemic Risk of the Global Financial Market?

The advance of cryptocurrencies has sparked wide concern over their interplay with the existing global financial market. This paper analyzes the risk spillover relation between cryptocurrencies and major financial assets, and unravels how cryptocurrencies could influence global financial systemic risk. We find that cryptocurrencies function as a separate risk source from traditional assets. Major legislative, financial and technological events in the cryptocurrency market may affect risk spillover dynamics. Although the overall penetration of cryptocurrencies is not yet deep, introducing cryptocurrency can significantly increase the systemic risk to traditional markets during low risk level episodes.     

基于遥感蒸散数据的农田灌溉水平评价和设施建设分区研究

[目的]利用遥感蒸散数据快速评价农田年度灌溉水平,及时获取基本农田建设成效,为农田灌溉设施分区建设提供参考。[方法]以黑龙江省海伦市为研究区,MOD16蒸散产品为数据源,计算有效灌溉量、灌溉需水量和作物缺水指数,分别对研究区2013年的旱地和水田灌溉水平进行评价,并将评价结果与农田水利设施作用分值图进行叠加分析,在此基础上进行基本农田灌溉设施建设分区。[结果]2013年海伦市耕地灌溉水平整体较高,区域间建设水平差别较大,可划分为4个级别。其中1级和2级灌溉水平的地块面积比例可达78.06%,地域分布上看,北部、西部、中部乡镇灌溉水平较高,东南部乡镇灌溉水平相对较低。叠加得出8种组合类型,分为设施修建型、设施提升型和设施维护型3种分区类型。[结论]研究为高标准农田建设提供了更详细的农田灌溉水平评价方法和更具差别化的农田灌溉设施建设策略。     

Transforming under deep uncertainty: A strategic perspective on risk management

Companies increasingly face the need for transformation in today’s rapidly changing business environment, characterized by major shifts in technology, regulation, and customer behavior. A lack of strategic risk insight and foresight leaves many incumbents insufficiently prepared in the face of such deep uncertainty. We argue that traditional risk management falls short because it predominantly focuses on strategy execution while leaving strategy formulation largely untouched. Moreover, an administrative-heavy risk management process can create strategic inertia and a misleading sense of control. In today’s dynamic business context, companies must not only increase the speed and impact of their strategy execution but also continuously explore the development of new strategies in response to disruptive events or emerging opportunities. Our research shows how leading companies develop a strategic risk management (SRM) capability to increase their resilience and agility in response to deep uncertainty. SRM takes a strategic, forward-looking perspective and focuses on strengthening processes, people, and practices for purposefully integrating risk into the strategy formulation process. This article offers a framework with three proven configurations of content and timing integration, risk management roles, and leading practices that enable effective SRM.     

基于生态位理论的浙江省区域农产品质量竞争力评价研究

[目的]把握区域农产品质量竞争力不仅是促进区域农产品富有竞争力的积极方法,也是稳步提升农产品质量安全的重要做法。[方法]文章运用生态位态势理论,构建了以区域为评价主体的农产品质量竞争力评价体系,并从两个层面对浙江省各地级市农产品质量竞争力进行研究。在纵向层面上,测算了各地级市农产品质量竞争力生态位宽度,对各地级市农产品质量竞争力状态进行了评价和排序。在横向层面上,测算了各地级市农产品质量竞争力生态位重叠度,对各地级市农产品质量竞争力的合作程度进行了评价与区分。最后整合运用生态位协同、生态位扩充等理论,提出浙江省各地级市提升农产品质量竞争力发展策略。[结果]通过区域农产品质量竞争力生态位宽度研究发现,杭州和宁波的农产品质量竞争力较强,其生态位宽度分别为0.103 6和0.103 2,而丽水和舟山农产品质量竞争力相对居后,其生态位宽度分别为0.079 0和0.075 6;通过区域农产品质量竞争力生态位重叠度研究发现,可将全省划分为西北平原区域、东南部沿海区域和西南部山地区域等3个区域,各区域内部地级市可通过协同合作提升农产品质量竞争力。[结论]根据各地级市生态位宽度和生态位重叠度的差异,应采取生态位扩充、生态位协同、生态位分离的策略,从而因地制宜提升区域农产品质量竞争力。     

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.