Liberalization,Regulatory Delays and Vulnerability to Systemic Banking Crisis

This work aims at contributing to the improvement of the early warning systems of banking crises using a new approach accounting for model uncertainty. We show that a multinomial logit model based on Bayesian model averaging (BMA) is a good strategy to predict banking crisis. To do this, we argue that differences in vulnerability to banking crisis can be largely explained by an asymmetry between financial market evolution and regulation update on a sample of 49 developed and developing countries between 1980 and 2010. When markets are liberalized, competition pushes bankers to take more risks and take advantage of regulatory delays thus increasing crises probabilities. Our empirical evidence supports that crisis probability is higher in country liberalizing their banking system when regulation is not updated. We developed an early warning system for systemic banking crises based on the multinomial logit model. Its main difference to existing prediction models and its contribution to the literature is that it is intended to identify and resolve what is called by Bussiere and Fratzscher [(2006). Towards a new early warning system of financial crises. Journal of International Money and Finance, 25(6), 953–973] as post-crisis bias in binomial models and to develop a new methodology of leading indicators selection based on BMA. Overall, our model predicts all banking crises during our sample period.     

Evaluating drug cost per responder and number needed to treat associated with lixisenatide on top of glargine when compared to rapid-acting insulin intensification regimens on top of glargine,in patients with type 2 diabetes in the UK,Italy, and Spain

Objectives: This study investigated the cost per responder and number needed to treat (NNT) in type 2 diabetes mellitus (T2DM) patients for lixisenatide compared to insulin intensification regimens using composite endpoints in the UK, Italy, and Spain.

Methods: Efficacy and safety outcomes were obtained from GetGoal Duo-2, a 26-week phase 3 trial comparing lixisenatide vs insulin glulisine (IG) once daily (QD) and three times daily (TID). Response at week 26 was extrapolated to 52 weeks, assuming a maintained treatment effect, based on long-term evidence in other T2DM populations. Responders were defined using composite end-points, based on an HbA1c threshold and/or no weight gain and/or no hypoglycemia. The HbA1c threshold was varied in sensitivity analyses. Annual treatment costs were estimated in euros (1 GBP?=?1.26 EUR), including drug acquisition and resource use costs. Cost per responder was computed by dividing annual treatment costs per patient by the proportion of responders.

Results: Lixisenatide was associated with the lowest cost per responder for all composite end-points that included a weight-related component. For the main composite end-point of HbA1c ≤7.5% AND no weight gain AND no symptomatic hypoglycemia, cost per responder results were: UK: 6,867€, 8,746€, and 12,410€; Italy: 7,057€, 9,160€, and 12,844€; Spain: 8,370€, 11,365€, and 17,038€, for lixisenatide, IG QD, and TID, respectively. The NNT analysis showed that, for every 6.85 and 5.86 patients treated with lixisenatide, there was approximately one additional responder compared to IG QD and TID, respectively.

Limitations: A limitation of the clinical inputs is the lack of 52-week trial data from GetGoal Duo-2, which led to the assumption of a maintained treatment effect from week 26 to 52.

Conclusions: This analysis suggests lixisenatide is an efficient economic resource allocation in the UK, Italy, and Spain.     

基于时间序列的BP神经网络预测鄂尔多斯市的GDP

国内生产总值(GDP)是制定经济发展战略、规划、年度计划以及各种宏观经济政策的重要依据,研究GDP预测方法具有重要的现实意义。在对鄂尔多斯市GDP数据序列分析的基础上,考虑到BP神经网络可以通过简单非线性函数的复合能够实现对于复杂函数的映射,本文采用BP神经网络方法来预测鄂尔多斯市的GDP,取得了较好的预测效果。     

基于遗传算法优化混沌神经网络的股票指数预测

为提高BP神经网络预测模型对混沌时间序列的预测准确性,提出一种基于遗传算法优化BP神经网络的改进混沌时间序列预测方法。本文采用时间序列输入输出参数数量构造BP神经网络拓扑结构,利用遗传算法优化BP神经网络的权值和阈值,然后训练BP神经网络预测模型求得最优解,将该预测方法应用到上证综合指数的时间序列进行有效性验证,结果表明了该方法对上证综合指数具有更好的非线性拟合能力和更高的预测准确性。     

论低碳产业集群的动力机制——基于省级面板数据的实证分析

发展低碳产业集群对于实现中国低碳经济目标有着极其重大的作用。本文论述了我国低碳产业集群动力机制的内涵,认为可持续发展观念是低碳产业集群的思想基础,政府的政策和制度创新对产业集群企业和公众的低碳活动起着激励作用,经济绩效是低碳产业集群的直接动力,技术创新是低碳产业集群发展的关键因素;而国内外低碳环境的压力和产业集群外公众的市场取舍与监督构成了外部动力。各种动力互相配合作用,推动了低碳产业集群快速发展。     

Positive and Negative Word of Mouth about Restaurants: Exploring the Asymmetric Impact of the Performance of Attributes

This study examines the relationship between attribute performance of a restaurant and customers' positive and negative word of mouth (WOM). We present an analysis of 168,262 customer reviews, which consist of an overall rating of a dining experience, ratings of food, physical environment and employee service, and real expenditure for a meal, totally covering 1,542 Chinese restaurants on a restaurant guide website. The results indicate that the performance of attributes has an asymmetric impact on positive and negative WOM for the restaurant industry as well as for low-end and mid-to-high-end restaurants. Our results also show that, in most cases, there is a combined effect of two attributes on both positive and negative WOM.     

A profitable trading and risk management strategy despite transaction costs

We present a new profitable trading and risk management strategy with transaction cost for an adaptive equally weighted portfolio. Moreover, we implement a rule-based expert system for the daily financial decision-making process using the power of spectral analysis. We use several key components such as principal component analysis, partitioning, memory in stock markets, percentile for relative standing, the first four normalized central moments, learning algorithm, and switching among several investment positions consisting of short stock market, long stock market and money market with real risk-free rates. We find that it is possible to beat the proxy for the equity market without short selling for 168 S&P 500-listed stocks during the 1998–2008 period and 213 Russell 2000-listed stocks during the 1995–2007 period. Our Monte Carlo simulation for both the various set of stocks and the interval of time confirms our findings.     

Fiscal Fan Charts: A Tool for Assessing Member States' (Likely?) Compliance with EU Fiscal Rules

This paper sets out a methodology for constructing fan charts for the government deficit and debt ratios over the medium term. It relies on information contained in Stability/Convergence Programme Updates, a model of the relevant stochastic process (for example, that of real GDP) or processes, and a parameter estimate of the sensitivity of the primary budget balance to the output gap for the member state under consideration. A model of the dynamic deficit–debt relationship allows the impact of random output growth to work its way through the fiscal arithmetic in a consistent and traceable way to produce fan charts over a five‐year forecast horizon. The initial set of fiscal fan charts included here for Ireland use the indicative public finance projections set out in its 2011 Update. The methodology makes the standard assumption of no fiscal policy response to any change in the budgetary position over the period such as could arise from changes in growth rates. Governments will, however, generally be in a position to adjust fiscal policy towards meeting a specific target, such as the 3 per cent Maastricht Treaty deficit target. A second set of fan charts is included that indicates how the probabilistic range of fiscal outcomes could be affected by a tightening of fiscal policy in 2013–15.     

Bounds on the estimation error in the chain ladder method

Buchwalder et al. (2006) have illustrated that there are different approaches for the derivation of an estimate for the parameter estimation error in the distribution-free chain ladder reserving method. In this paper, we demonstrate that these approaches provide estimates that are close to each other for typical parameters. This is carried out by proving upper and lower bounds.     

基于组合预测模型的我国能源需求预测分析

本文根据预测理论,结合我国历年能源消费的相关数据,分别采用多元线性回归方法、灰色预测、指数模型方法建立我国能源需求的单项预测模型,并对各单项模型的结果进行分析比较和检验,然后采用误差平方和最小法进行权重分配,建立了我国未来能源需求量的组合预测模型,最后,应用该模型对我国未来10年的能源需求量进行预测,结果表明：组合预测的精度要远远优于单项预测;我国未来10年的能源需求仍呈现较快的增长趋势。