The Limitations of Bankruptcy Prediction Models: Some Cautions for the Researcher

The purpose of this study is to demonstrate potential problems associated with the use of bankruptcy prediction models in current research. The tests in this study demonstrate the problems that may arise when bankruptcy prediction models are inappropriately applied. This analysis evaluated the Zmijewski (1984) and Ohlson (1980) models using time periods, industries, and financial distress situations other than those used to originally develop the models. The findings indicated that both models were sensitive to time periods. That is, the accuracy of the models declined when applied to time periods different from those used to develop the models. The findings also suggest that the accuracy of each model continues to decline moving from the 1988–1991 to the 1992–1999 sample period. Additionally, Ohlson's (Zmijewski's) model was (was not) sensitive to industry classifications. The findings of this study also suggest that the Ohlson and Zmijewski models are not sensitive to financial distress situations other than those used to develop the models. Thus, the models appear to be more generally useful for predicting financial distress, not just bankruptcy.In sum, the results of this study suggest that researchers should use bankruptcy prediction models cautiously. Applying the models to time periods and industries other than those used to develop the models may result in a significant decline in the models' accuracies. Additionally, some bankruptcy prediction models may be more appropriate for evaluating various forms of financial distress as opposed to just bankruptcy. To avoid erroneous applications of bankruptcy prediction models in the future, it is necessary for researchers not only to understand the uses of prediction models, but also to understand the limitations of the models.     

消力池底板上举力的神经网络模型预测

利用BP人工神经网络的非线性映射功能,结合水槽中两种主要消能形式的模型试验,以流场中作用在板块上表面实测脉动压力为变量,构建了一个简单的上举力预测系统.经过实测资料验证,该系统预测效果较好.本文的研究成果对消力池安全实时监测具有一定的意义.     

滑坡预报研究综述

边坡的失稳演化与其内部结构和外界因素的影响密切相关,在回顾滑坡预测发展史的基础上,指出滑坡预报包括空间预测、强度预测和时间预报三方面内容,而时间预报是滑坡预报的核心.探讨了滑坡预报存在的问题与现阶段经验预报的必要性.     

三峡水库重庆段整体水质变化趋势分析

三峡水库蓄水以后,为了研究三峡库区水质如何变化这一国内外广泛关注的问题,通过对三峡水库重庆段近几年水质数据的分析,采用季节性Kendall检验法分别对库区干流6个水质监测控制断面和主要支流的3个代表断面近6 a（2003~2008年）的主要污染指标总磷、高锰酸盐指数和石油类进行水质趋势分析。结果显示,库区江段水质参数浓度趋势以下降变化为主,总体水质状况6 a来基本趋于好转;库区重庆江段受总磷污染较为严重,且以呈上升变化为主,应加强对其污染源的控制和治理。     

三峡水库蓄水前后干流水质特征与变化趋势研究

为了分析三峡水库蓄水前后水质的变化情况,选取三峡水库干流5个代表断面,通过2010年现状水质调查监测结果与1998~2009年历史水质资料的比较,深入分析了三峡水库蓄水前后干流水质特征和变化趋势。研究表明：目前三峡水库干流水质良好,年度水质为Ⅲ类;蓄水前以Ⅲ类为主,蓄水后以Ⅱ~Ⅲ类为主;从水库上游至下游,沿程水质逐渐趋好;蓄水后,总磷、高锰酸盐指数、铅等是影响水质的主要因子,与蓄水前相比,其沿程降低趋势更强,水质超标污染状况有所缓解。     

一种基于BP神经网络的江河潮位短期预测

江河涌潮与海洋潮汐相比较,其潮位和潮时有着与海洋潮汐规律相同的一面,但它与海洋潮汐所不同的是,其更容易受到天文、气象等因素的影响,有着异于海洋潮汐的不稳定性.涌潮的潮位值变化就是各种因素综合结果的表现.通过借助BP神经网络可以逼近任意非线性函数的能力和特点,构建一个用于短期预测潮水潮位的模型,利用一小部分潮位的历史数据...     

全球丙烯供需状况及其发展趋势分析

介绍了全球丙烯的供需状况、消费构成及贸易状况；分析了全球丙烯资源的来源渠道及地区分布状况；在此基础上总结出有关全球丙烯生产及消费的发展趋势，并对我国丙烯生产提出了一些看法。     

黑河流域径流变化规律及趋势预测

根据黑河1956—2012年各水文站径流量数据,应用Mann-Kendall非参数检验法对黑河流域径流变化规律进行了分析,并利用BP神经网络对未来黑河径流演变趋势进行了预测。结果表明:1黑河干流及主要支流汛期来水量占年径流量的比例较大,为63.18%~94.56%,出山口径流量未来总的趋势为增加;2Mann-Kendall趋势检验结果表明莺落峡、祁连、新地及嘉峪关站未来径流预测趋势与实测序列趋势较为一致,而札马什克站未来径流预测趋势和实测序列趋势相反,但该站M-K值的绝对值逐渐变小并趋于0,可以推测未来该站径流量趋势将由减少转为增加,这与BP神经网络预测的结果一致。     

Experimental study of flow patterns and pressure drops of heavy oil-water-gas vertical flow

A stainless steel apparatus of 18.5 m high and 0.05 m in inner diameter is developed, with the heavy oil from Lukeqin Xinjiang oil field as the test medium, to carry out the orthogonal experiments for the interactions between heavy oil-water and heavy oil-water-gas. With the aid of observation windows, the pressure drop signal can be collected and the general multiple flow patterns of heavy oil-water-gas can be observed, including the bubble, slug, churn and annular ones. Compared with the conventional oil, the bubble flows are identified in three specific flow patterns which are the dispersed bubble (DB), the bubble gas-bubble heavy oil, and the bubble gas-intermittent heavy oil (B_g – I_o). The slug flows are identified in two specific flow patterns which are the intermittent gas-bubble heavy oil (I_g – B_o) and the intermittent gas-intermittent heavy oil (I_g – I_o). Compared with the observations in the heavy oil-water experiment, it is found that the conventional models can not accurately predict the pressure gradient. And it is not water but heavy oil and water mixed phase that is in contact with the tube wall. So, based on the principle of the energy conservation and the kinematic wave theory, a new method is proposed to calculate the frictional pressure gradient. Furthermore, with the new friction gradient calculation method and a due consideration of the flow characteristics of the heavy oil-water-gas high speed flow, a new model is built to predict the heavy oil-water-gas pressure gradient. The predictions are compared with the experiment data and the field data. The accuracy of the predictions shows the rationality and the applicability of the new model.     

长荡湖水环境变化趋势及其主要影响因子

采用Mann-Kendall非参数统计检测法对长荡湖长序列水质参数进行分析,再利用模糊综合评价法评价历年水质状况,同时找出导致水质变化的主要影响因子。趋势分析表明：长荡湖pH、EC、DO、BOD 、COD、TP、CODMn、SS呈上升趋势,SD、NH3-N、TN、粪大肠杆菌群数和石油类呈下降趋势,其中SS近年急剧上升, SD下降,耗氧类参数上升明显,说明长荡湖近8年水质总体呈下降趋势。突变分析表明：SS有1个突变点位于极显著信度线（α＝0.01）之外,SS、BOD、COD还各有1个突变点位于显著信度线（α＝0.05）与极显著信度线之间,其余突变点均位于显著信度线之间。对突变次数及时间进行分析,结果表明：COD突变最复杂,SD和石油类变化趋势最简单,突变主要集中在2006、2008和2012年。模糊综合评价结果显示长荡湖水质2005—2009年变化不大,2009年后水质由Ⅲ类变为Ⅳ类,其主要影响因子为BOD 、 COD、TN和CODMn。