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《International Journal of Forecasting》2020,36(3):1092-1113
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. 相似文献
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为了解不同园林树木对重金属的吸收情况,以北京市陶然亭公园、紫竹院公园、中科院植物园、马甸公园、皇城根遗址公园和营城建都滨水绿道不同环境绿地为样地,选择样地间共有的园林树木,采用ICP光谱仪测定了6种树木叶片和一年生枝条中重金属锌(Zn)、铬(Cr)、镍(Ni)、砷(As)和汞(Hg)的含量,以此为基础,采用隶属函数法将6种树木单位重量对5种重金属的综合富集能力进行排序,并对同树种叶片与当年生枝、5种重金属吸收量分别进行相关分析。结果表明:1)不同树种叶片和一年生生枝条单位重量中重金属含量有显著差异,且因重金属种类而异。Zn、As含量最高的为金银木,Cr、Ni、Hg含量最高的依次为圆柏、侧柏、丁香;丁香富集Zn、Cr、As的能力最低,白皮松富集Ni能力最低,侧柏富集Hg能力最低。2)树木叶片和当年生枝单位重量对5种重金属综合富集能力排序为:金银木>侧柏>圆柏>油松>丁香>白皮松。其中,针叶树种中圆柏、侧柏、油松富集5种重金属的综合能力显著高于白皮松(P<0.01),阔叶树种中金银木吸收重金属的能力显著高于丁香(P<0.01)。3)树木不同器官中重金属含量也不同,不同树种间叶片与枝条中仅Cr含量具有显著相关性,相关系数为0.461(P<0.01),其他4种元素均未达到显著水平。4)同树种叶片和当年生枝Cr和Ni吸收呈极显著相关性(P<0.01),其他元素吸收之间未呈现相关性。本研究结果对北 相似文献
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《Socio》2019
Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models. 相似文献
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湿陷性黄土地区绿化带需灌溉,如果灌溉水量过大将会导致湿陷性土层的含水量增大,当含水量增大至湿陷起始含水量时,湿陷性土层开始产生湿陷现象。为了防止湿陷性黄土地区因大量灌溉水进入土中,导致湿陷,本文根据湿陷性土层的湿陷机理及湿陷性黄土的本构关系,提出湿陷性土层上绿化区域防止湿陷的具体方法。 相似文献
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《International journal of injury control and safety promotion》2013,20(4):408-415
ABSTRACTThis paper presents a fuzzy logic model to support decisions for the suggested speed limit for sections of highways as they become affected by adverse road conditions, environmental factors and differing traffic levels. The definition of the main factors, and their weight, that reduce safety on highways was conducted by the Delphi method, consulting traffic engineering experts. The survey served as a basis for structuring indicators and indexes in a soft decision tree, structured by a fuzzy logic system. The system can be used to support the decisions of those that manage the roads, indicating to drivers’ speeds, by visual, audible or combined systems. 相似文献