基于特征增量的新类识别方法研究

在分类应用的过程中,经常会出现新的类别,导致数据分布发生显著变化,使得原分类模型不再适用。如何识别新的类别使分类模型能适应其出现已经成为一个亟需解决的问题。本文提出基于特征增量的SVDD(支持向量数据描述)新类识别方法。该方法在SVDD算法的基础上,通过增加新特征,扩大特征空间维度从而提高模型对于新类的识别能力。在多个数据集上的实验结果表明,该方法能有效识别新类,使更新后的模型具有更高的准确度。     

Using Machine Learning for measuring democracy: A practitioners guide and a new updated dataset for 186 countries from 1919 to 2019

We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016). Four improvements are particularly notable: First, we produce a continuous and a dichotomous version of the Machine Learning democracy indicator. Second, we calculate intervals that reflect the degree of measurement uncertainty. Third, we refine the conceptualization of the Machine Learning Index. Finally, we significantly expand the data coverage by providing democracy indices for 186 countries in the period from 1919 to 2019.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

要素禀赋、政策支持与金融资源配置:产业转移的承接模式研究

本文以商务部认定的湖南、江西省四个加工贸易梯度转移重点承接地为样本,对比分析要素禀赋、政策支持和金融资源配置的异同及其影响,在总结归纳各自承接模式的特点的基础上,提出了"精品园区+核心企业+直接融资+金融助推"的较优模式,认为政策支持是影响承接产业转移的直接因素;要素禀赋的影响力有弱化趋势;金融资源配置是影响承接力和产业结构优化升级的重要因素;对待处于不同生命周期的产业转移企业,承接的工作重点应各有侧重.因此,承接产业转移要与"转方式、调结构"的战略方向一致.注重与本地的产业互补相结合,并需要金融部门的主动参与和服务创新.     

A Generalized Nash Equilibrium network model for post-disaster humanitarian relief

We develop a Generalized Nash Equilibrium network model for post-disaster humanitarian relief by nongovernmental organizations (NGOs). NGOs derive utility from providing relief supplies to victims of the disaster at demand points in a supply chain context while competing with each other for financial funds provided by donations. The shared constraints consist of lower and upper bounds for demand for relief items at the demand points to reduce materiel convergence or congestion. This game theory problem is reformulated as an optimization problem and numerical examples and a theoretical case study on Hurricane Katrina given.     

Adding Value to Applied Policy Models： The Case of the WTO and OECD Support Classification Systems

Both the OECD and the WTO have accumulated systematic data on the magnitude of support going to farmers as a result of farm policies. The datasets are collected for different purposes, but both give a detailed picture of the evolution of these policies. This paper extends recent work on the compatibility or otherwise of the two attempts at policy monitoring by considering the categorization of individual policy instruments in Norway, Switzerland, the US, and the EU. The results show how the OECD dataset, particularly with respect to the link between direct payments and production requirements, complements that of the WTO. Many payments classified in the WTO Green Box require production, raising the possibility that they may distort production and trade. Though the issue of correct notifications to the WTO is the province of lawyers, the implications for modeling and policy analysis are of interest to economists, and the broader question of improving the consistency of the two datasets is of importance in the quest for transparency in the interpretation of changes in farm policies.     

以更新的理念与手段应对新的养老问题——广西从家庭养老向社会化养老过渡的对策研究

今后一定时期,广西总体上从传统家庭养老向现代社会化养老过渡将是大势所趋。应该在推广养老保险等社会保障体系,以及居家养老加社区服务方式的同时,结合广西区情,参考区内外的经验做法,从四个方面采取更新的对策手段:政策更新——加强政府在社会养老方面的政策倾斜与扶持力度;服务更新——建立新型职业化与义务化结合的养老服务队伍体系;产业更新——从四个主要层面发展"银发产业";观念更新——倡导适应新形势的养老新观念。     

准确把握当前疫情防控新形势新任务 全力抓好金融支持疫情防控和复工复产工作

新冠肺炎疫情发生以来,浙江省金融系统积极响应,快速落实,对内严格疫情排查防控,对外做实金融服务保障,及时出台疫情防控金融服务政策措施,精准用好专项再贷款资金,主动对接疫情防控领域重点企业生产资金需求,全力构建本外币政策绿色通道,充分发挥金融在疫情防控工作中的重要作用。当前,我省疫情防控工作已经进入新阶段,全省金融系统要进一步巩固阶段性成果,切实帮扶受疫情影响的困难企业,全面做好复工复产金融服务,坚决打赢疫情防控阻击战。