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首页> 外文学位 >Sovereign nations financial distress: An early warning system for predicting Paris Club debt re-scheduling events from financial ratios, and neural network indexing model.
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Sovereign nations financial distress: An early warning system for predicting Paris Club debt re-scheduling events from financial ratios, and neural network indexing model.

机译：主权国家的财务困境：一种从财务比率和神经网络索引模型预测巴黎俱乐部债务重新安排事件的预警系统。

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Government leaders and Bretton Woods institutions managers have called for the development of early warning systems for predicting financial stress of nations. This research addresses that problem by drawing on theories from commercial bankruptcy prediction, and finding a similarity between corporate financial ratios and those of nations, and also finding a similarity between chapter XI corporate restructuring and a Paris Club event, in which multilateral debt is rescheduled to achieve a sustainable debt service status.; Grounded in the literature, four sovereign nation ratios relative to creditworthiness, liquidity, ability to pay, and economic activity were selected. Such ratios include elements of GNP, short-term and total debt, export earnings, internal and external reserves, and debt service requirements. With the exception of the creditworthiness ratio, the study found a significant difference between Paris Club nations and those not defaulting.; A ratio-based neural network was trained on 90 observations from 40 defaulting nations for the period 1989 to 1993, and 45 observations from 45 non-defaulting debtor nations for 1993. The trained neural network was then tested on all 208 nations in the World Bank STAR database for 1994. The neural network was programed to classify 208 nations into probable defaulting and non-defaulting groups, 1994 to 1996.; The neural network correctly classified 77% of defaulting and non-defaulting nations. Its output weights were found to act, look and feel like Zeta-Scores used in corporate bankruptcy classification. On a range of values from 0.0 to 1.0, a nations' neural weight of {dollar}<{dollar}0.73 successfully separated 95% of Paris Club and non-Paris Club nations.; IMF and World Bank policies, and how these changed over recent decades, are discussed, together with social objectives of default prevention, poverty alleviation, and the perspectives of donor nations and debtor nations alike. Future research should address shorter prediction horizons with increased emphasis on commercial debt.
机译：政府领导人和布雷顿森林体系的管理者呼吁建立预警系统，以预测各国的财务压力。该研究通过利用商业破产预测中的理论，并在公司财务比率与国家财务比率之间找到相似之处，以及在第十一章公司重组与巴黎俱乐部事件之间找到相似之处来解决该问题，在该事件中，多边债务被重新安排为达到可持续的偿债状态。根据文献，选择了四个相对于信用度，流动性，支付能力和经济活动的主权国家比率。这些比率包括国民生产总值，短期和总债务，出口收入，内部和外部储备以及还本付息的要素。除信誉度比率外，研究发现巴黎俱乐部国家与未违约的国家之间存在显着差异。基于比率的神经网络接受了1989年至1993年期间来自40个违约国家的90个观察结果的训练，并接受了1993年来自45个非违约债务国的45个观察结果的训练。然后在世界银行的所有208个国家中对训练后的神经网络进行了测试。 1994年的STAR数据库。对神经网络进行了编程，将1994年至1996年的208个国家分为可能的违约和非违约组。神经网络正确地将77％的违约和非违约国家分类。发现其输出权重的行为，外观和感觉就像公司破产分类中使用的Zeta-Scores。在0.0到1.0的范围内，一个国家的神经元权重{dollar} <{dollar} 0.73成功地将95％的巴黎俱乐部和非巴黎俱乐部国家分开。讨论了基金组织和世界银行的政策，以及最近几十年来这些政策的变化，以及预防违约，减轻贫困的社会目标，以及捐助国和债务国的观点。未来的研究应针对更短的预测范围，并更加重视商业债务。

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