Assessing the historical role of credit: Business cycles,financial crises and the legacy of Charles S. Peirce

This paper provides a historical overview of financial crises and their origins. The objective is to discuss a few of the modern statistical methods that can be used to evaluate predictors of these rare events. The problem involves the prediction of binary events, and therefore fits modern statistical learning, signal processing theory, and classification methods. The discussion also emphasizes the need for statistics and computational techniques to be supplemented with economics. The success of a forecast in this environment hinges on the economic consequences of the actions taken as a result of the forecast, rather than on typical statistical metrics of prediction accuracy.     

Preference aggregation and atoms in measures

This paper examines the aggregation of preferences with a finitely additive measure space of agents. We consider three types of non-dictatorship axioms: non-dictatorship, coalitional non-dictatorship, and atomic non-dictatorship. First, we show that the existence of an atom is a necessary and sufficient condition for the existence of a social welfare function that satisfies weak Pareto, independence of irrelevant alternatives, and coalitional non-dictatorship. Second, we simultaneously impose non-dictatorship and coalitional non-dictatorship, and specify a necessary and sufficient condition for the finitely additive measure that guarantees the compatibility among the axioms. Third, we impose all non-dictatorship axioms and show that the corresponding measure is extremely restricted.     

因子分析中公因子提取方法的比较与选择

近代和现代统计分析方法中，因子分析是最重要的方法之一。因子分析中有7种提取公因子的方法，其中主成分法、极大似然法和主轴因子法是常用的方法。在着重解析这三种方法基本数学过程的基础上，对其适用条件和应用注意事项进行了比较，最后结合实例比较了不同公因子提取方法的结果，并对提取公因子过程中出现的问题给出了可能的解决办法。     

我国银行理财业务发展趋势研究

对国内商业银行理财业务的发展状况进行了简要概述， 进而对理财业务开展过程中的一些问题进行了分析， 强调指出市场化应该成为银行理财业务转型升级和创新发展的方向， 并提出了实现市场化发展方向的若干具体措施。     

Brexit: Tracking and disentangling the sentiment towards leaving the EU

On 23 June, 2016, the UK held a referendum to decide whether to stay in the European Union or leave. The uncertainty surrounding the outcome of this referendum had major consequences for public policy, investment decisions, and currency markets. We discuss some of the subtleties involved in smoothing and disentangling poll data in light of the problem of tracking the dynamics of the intention to Brexit, and propose a multivariate singular spectrum analysis method that produces trendlines on the unit simplex. The trendline yield via multivariate singular spectrum analysis is shown to resemble that of local polynomial smoothing, and singular spectrum analysis presents the nice feature of disentangling the dynamics directly into components that can be interpreted as changes in public opinion or sampling error. The merits and disadvantages of some different approaches for obtaining smooth trendlines on the unit simplex are contrasted, in terms of both local polynomial smoothing and multivariate singular spectrum analysis.     

Crime prediction by data-driven Green’s function method

We develop an algorithm that forecasts cascading events, by employing a Green’s function scheme on the basis of the self-exciting point process model. This method is applied to open data of 10 types of crimes happened in Chicago. It shows a good prediction accuracy superior to or comparable to the standard methods which are the expectation–maximization method and prospective hotspot maps method. We find a cascade influence of the crimes that has a long-time, logarithmic tail; this result is consistent with an earlier study on burglaries. This long-tail feature cannot be reproduced by the other standard methods. In addition, a merit of the Green’s function method is the low computational cost in the case of high density of events and/or large amount of the training data.     

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.     

Contradictions of the Climate-Friendly City: New Perspectives on Eco-Gentrification and Housing Justice

As local governments and corporations promote ‘climate friendliness’, and a low-carbon lifestyle becomes increasingly desirable, more middle- and upper-income urban residents are choosing to live near public transit, on bike- and pedestrian-friendly streets, and in higher-density mixed-use areas. This rejection of classical forms of suburbanization has, in part, increased property values in neighborhoods offering these amenities, displacing lower-income, often non-white, residents. Increased prevalence of creative and technology workers appears to accelerate this trend. We argue that a significant and understudied socio-environmental contradiction also occurs where the actual environmental outcomes of neighborhood transformation may not be what we expect. New research on greenhouse gas emissions shows that more affluent residents have much larger carbon footprints because of their consumption, even when reductions in transportation or building energy emissions are included. We describe an area in Seattle, Washington, the location of Amazon's headquarters, experiencing this contradiction and show a distinct convergence of city investments in low-carbon infrastructure, significant rises in housing prices and decreases in lower-income and non-white residents. We conclude with a discussion of a range of issues that require more attention by scholars interested in housing justice and/or urban sustainability.     

Tent City: Patterns of Informality and the Partitioning of Sacramento

Unsanctioned tent cities are increasing in number in cities throughout the western United States. Scholars explain the phenomenon as homeless people asserting their ‘right to the city’ or as ‘managed marginality’. These explanations capture much of the socio-political relationship between local government and homeless populations, but do not explain the long-term persistence of tent cities and the fluctuating nature of their visibility. A spatial history of informal encampments in Sacramento at three key moments—the founding of the city, the Great Depression and the Great Recession—reveals a long-term ebb and flow of tent cities occupying close-to-the-center, urban vacancies. Urban vacancies arise from the partitioning of the city into specific purposes, places and people, a taken-for-granted perception of how cities should be. The visibility of tent cities disrupts this aesthetic notion of stability and growth as homeless people use the tent to protest their isolation and exclusion.