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.     

The effect of economic policy uncertainty on China’s housing market

This study examines the relationship between economic policy uncertainty (EPU) and housing price at the macro-level in China as a developing country. The empirical evidence indicates that EPU has a leading effect on China’s housing market. In general, the housing market is prosperous when economic policy is stable and there is a positive relationship between housing price variation and EPU, which means housing market risk grows under unstable economic policies in this developing economy. Moreover, economic policy variation affects low-amplitude changes in housing prices. A variation of policy uncertainty enhances the risk premium of the housing market. By contrast, the level of EPU influences high-amplitude changes in housing prices, which reflects the trend of EPU dominating China's housing market.     

Is China’s Manufacturing Industry Efficient? Evidence from an Energy-Rebound Effect Perspective

Majority of the increase in global energy consumption is from China; hence, studying energy issues, especially in China’s manufacturing industry (CMI), is worthwhile and of much interest in the academic field. Based on the translog cost function, we develop a research framework to study the rebound effect of CMI. Considering the effect of asymmetric energy price, we augment the energy-cost function with asymmetric influence constraint of energy price. Again, we add time series data of CMI’s capital, labor, energy, and mid-input to the model to calculate the direct rebound effect of CMI. We find that the rebound effect of CMI is 44.2%, and CMI still has large energy-conservation potentials. Based on the results of this study, some policy recommendations are provided.     

Productivity efficiency analysis of the airlines in China after deregulation

Using data envelopment analysis and the Malmquist productivity index, this paper studies productivity efficiency of various airlines in China after the Chinese government deregulated the aviation industry in 2005. The results show that the productivity efficiency of non-state-owned airlines improves rapidly and eventually exceeds state-owned airlines after the deregulation policy. Among the state-owned airlines, the productivity of the local airlines and their technical changes are better than those of the central airlines.     

A new hub and spoke system proposal: A case study for Turkey's aviation industry

In this paper, we are proposing a multi-objective mathematical model for the selection of a newly constructed hub and spoke system. The objectives of this model are maximizing aircraft utilization and revenue whilst reducing the commercially infeasible network detour factor. The sensitivity analysis of the model is tackled using weights related to the objectives as well as the network detour factor. The number of available aircraft and the range that an aircraft can reach are also considered in this model. Since Istanbul has already got a hub and spoke system with busy airports on both sides of the city, the model is applied to three other major cities of Turkey, Ankara, Antalya, and Izmir. The test data consists of over 90 cities in Europe and in the Middle East. The data includes unit passenger revenues and operating costs for the segments, distances between cities and hubs, expected load factors and flying times of segments. The scenarios are tested under specific expectations of airline network experts and the results are visualized by using Pareto front graphs. Compared to other candidates, Antalya stands out as a good choice for a new hub and spoke system in Turkey. The results of this model could be helpful for airlines and other airports in Turkey in order to identify their potential and competitive position in relation to their counterparts.     

Partner‐diversity effects on alliance termination in the early stage of green alliance formation: Empirical evidence from carbon‐emission reduction projects in Latin America

Partner diversity is a key influencer in interorganizational alliances, and several empirical studies have shown that its outcomes are contingent on alliance‐specific factors. We extend this research as well as the growing literature on green alliances, in which partner diversity is uniquely high. Specifically, we examine partner‐diversity effects on alliance termination in the early stage of green alliance formation. We hypothesize that in this context, size disparity increases termination likelihood, whereas organizational variety and cultural separation have the opposite effect. To test our hypotheses, we use a sample of 366 alliance projects located in Latin America and submitted to the Kyoto Protocol's Clean Development Mechanism for evaluation, validation, and registration from 2004 to 2014. Our findings contribute to several research streams and provide practical guidance for successful formation of alliances focused on environmental protection.     

Bringing back Thrift Week: Neo-liberalism and the rediscovery of thrift

The paper examines and compares the movements for promoting working-class savings in the modern USA and in Great Britain in the 19th century. It explores the use of savings as a technology for managing individuals’ behaviour and motives and the nature of the government objectives which are served by these parallel projects.