Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem

Financial distress prediction (FDP) is a significant issue investigated by researchers, credit institutions and banks. Although extensive research has been conducted in this area, applications of combined feature selection (FS) methods and classification models are subjects that have been addressed intensely in recent years. One of the most important issues in the FDP problem is to employ an effective FS algorithm, leading to an acceptable level of performance accuracy in the implementation stage. Hence, this study primarily attempted to introduce a precise FS model and compared the obtained results with those of other conventional models tackling FDP in terms of accuracy. The proposed method involved the sequential floating forward selection (SFFS) algorithm applied as a wrapper FS technique to determine the best subset of features. At the classification stage, the support vector machine (SVM), owing to its good performance, demonstrated in numerous studies, in solving classification problems, was deployed. The performance of the proposed method was compared with those of other current well-known FS methods including artificial bee colony (ABC), genetic algorithm (GA) and sequential forward selection (SFS) (all of which are categorized under wrapper methods), and principal component analysis (PCA), relief and information gain (IG) (best known as filter techniques) for our given datasets. The results indicated that a combined model of SVM based on the SFFS approach can yield greater accuracy than the other methods applied for our defined domestic and foreign datasets. Therefore, the SFFS-SVM ensemble classifier can be considered a promising addition to existent models when confronting the FDP issue.     

Earnings Volatility and Earnings Prediction: Analysis and UK Evidence

This paper confirms that US evidence of a negative relationship between earnings persistence and earnings volatility applies to UK firms over the period 1991–2010. Our analytical framework highlights the possibility that this result may reflect downward estimation bias in earnings persistence (and persistence of cash flow and accruals components of earnings) related to transitory earnings elements. Out‐of‐sample forecasts, based on models estimated for earnings volatility quartiles, suggest significant improvement in earnings forecasts for lower volatility firms. The results also suggest that the negative association between earnings persistence and volatility may be due to both estimation bias and variation in core earnings persistence.     

A Location Recommender Based on a Hidden Markov Model: Mobile Social Networks

With the development of mobile communication technology and location-based services, people can share information with friends through checking in anywhere, at any time. If we can “speculate” when users will next check in, we can make relevant and useful recommendations. Here, we introduce a new check-in-based hidden Markov model to cope with changing circumstances. A certain check-in-based hidden Markov model for each group is obtained first. The model then analyzes temporal check-in intervals of users before suggesting locations. We also discuss optimal parameter settings for the number of hidden states and the corresponding number of user groups. Experiments show that, given observations of a new entrant, the model is able to predict the most probable time period the user will check in next time. It can also recommend a specific user group for the new entrant. Hence, it enables the recommendation of potential locations of interest for the new entrant.     

Uncertain times and early predictions of bank failure

The Great Financial Crisis shows that bank failure in the United States, while rare, is a concern during uncertain times. Interest here is in the ability to predict future failures at the start of a crisis, when the recent past has few events on which to base inferences. I show that policy makers using estimates based on the Savings and Loans crisis would identify in early 2009 that 2.0% of banks were in critical condition and 7.0% were unhealthy. This is comparable to the 1.7% of banks that failed within a year and the 3.9% of banks that would fail during the crisis.     

新能源汽车对铜箔产业的影响分析及预测

从新能源汽车的发展趋势自上而下的分析了新能源汽车对锂电铜箔产业的影响。首先分析和预测了新能源汽车的前景及未来4年的销量，然后对锂电池的三大消费领域进行了结构分析，分别预测了未来发展势头和需求量。其次对锂电铜箔的需求量进行了换算和预测，对供给方面进行了转产和新增产能可能性分析，并统计了产能数据结合产能利用率进行了预测。最后根据供需差额分析了锂电铜箔行业景气程度，对该产业投资者提出了合适的对策建议。     

A Study of Social Network Effects on the Stock Market

Consumer confidence is an economic indicator that measures the degree of optimism that consumers feel about the overall state of the economy as well as their personal financial situation. The authors measure consumer sentiment via analysis of social networks and show that such sentiment affects stock prices; specifically, the S&P 500 and the Dow Jones Industrial Average. Shiller, Fischer and Freidman [1984], Fisher and Statman [2003], and Bremmer [2008] also examine the influence of consumer sentiment, measured from Conference Board data, on the stock market. The authors add to this literature by creating a measure of consumer confidence by utilizing Twitter data and by examining the relationship between our measure of consumer sentiment and the S&P 500 and the Dow. They implemented lexicographic analysis of Twitter data over a three-month period and found that talk intensity of economic issues not only causes shifts in the daily stock market prices, but also has a significant negative effect.     

湖北省地级市KIBS发展现状及预测

KIBS是国家创新系统的重要部分,担负着知识的交流、整合、创新的循环过程。近几年,湖北省地级市KIBS从业人员总数增加,KIBS增加值增加,问题是KIBS增加值的增长速度低于从业人数增长速度。其根本原因是KIBS从业人员的行业技能、专业知识不能满足KIBS发展的需求。提升知识密集型服务业的质量,湖北省政府不仅要创造环境和条件,及时出台促进KIBS行业发展的法律法规,鼓励龙头企业大力发展KIBS行业起模范带头作用,完善计算机软硬件设施,也要有对教育事业进行改革的决心,提升学生的素质和专业技术知识能力,为KIBS更好发展提供人才支撑。     

煤层气井压裂产量预测方法

通过调研煤层气井压裂产量预测方法原理及优缺点,优选出典型曲线产量预测法具有较强的适用性,对未投产井以及投产井都能预测,预测结果与实际井生产数据对比,准确度较高。     

新的WBO算子及其在城市道路网交通事故预测中的应用研究

以有效提高系统特征数据序列的光滑性为目的,在已有缓冲算子研究的基础上,根据新息优先原理,构建了一个新的WBO(Weakening Buffer Operator),克服了现有算子对新信息利用不充分的缺陷,并通过城市道路网交通事故预测案例与已有的系列WBO进行了弱化效果的比较。研究结果表明,受扰动因素冲击后的系统特征序列经新WBO作用后,建模精度得到了显著提高。     

Assessing the road safety impacts of a teleworking policy by means of geographically weighted regression method

Travel demand management (TDM) consists of a variety of policy measures that affect the effectiveness of transportation systems by changing travel behavior. The primary objective of such TDM strategies is not to improve traffic safety, although their impact on traffic safety should not be neglected. The main purpose of this study is to simulate the traffic safety impact of conducting a teleworking scenario (i.e. 5% of the working population engages in teleworking) in the study area, Flanders, Belgium. Since TDM strategies are usually conducted at a geographically aggregated level, crash prediction models should also be developed at an aggregate level. Given that crash occurrences are often spatially heterogeneous and are affected by many spatial variables, the existence of spatial correlation in the data is also examined. The results indicate the necessity of accounting for the spatial correlation when developing crash prediction models. Therefore, zonal crash prediction models (ZCPMs) within the geographically weighted generalized linear modeling framework are developed to incorporate the spatial variations in association between the number of crashes (including fatal, severe and slight injury crashes recorded between 2004 and 2007) and other explanatory variables. Different exposure, network and socio-demographic variables of 2200 traffic analysis zones (TAZs) are considered as predictors of crashes. An activity-based transportation model framework is adopted to produce detailed exposure metrics. This enables to conduct a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models. In this study, several ZCPMs with different severity levels and crash types are developed to predict crash counts for both the null and the teleworking scenario. The results show a considerable traffic safety benefit of conducting the teleworking scenario due to its impact on the reduction of total vehicle kilometers traveled (VKT) by 3.15%. Implementing the teleworking scenario is predicted to reduce the annual VKT by 1.43 billion and the total number of crashes to decline by 2.6%.