Evaluating changes in credit rating quality of U.S. farmer cooperatives

Gerald Mashange, Allen M. Featherstone, Brian C. Briggeman
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引用次数: 2

Abstract

The default of a cooperative has significant implications on cooperative members and the agricultural supply chain. Therefore, monitoring and even predicting future changes in creditworthiness is of value to cooperative managers and their lenders. However, we know little about farmer cooperatives' credit profiles and behavior because their financial statements are seldom shared. Using a Moody’s credit rating model and a unique data set, this article estimates Markov chains to evaluate changes in farmer cooperatives’ credit quality. The unconditional (one-size-fits-all) probability matrix, as is typically estimated, is shown to not be appropriate in describing credit rating transitions. Results also show cooperatives do not exhibit rating change momentum since a downgrade is not likely to be followed by another downgrade in the next period. Credit ratings of farmer cooperatives with less than $20 million in net sales follow a first-order Markov chain with stationary probabilities and the cooperatives with net sales of more than $250 million follow a zero-order Markov chain. This article adds to the limited research available on the credit rating behavior of farmer cooperatives. Cooperative managers, directors, and lenders can utilize these findings to make more informed decisions to impact future credit ratings.

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评估美国农民合作社信用评级质量的变化
合作社违约对合作社成员和农业供应链产生重大影响。因此,监测甚至预测未来信用度的变化对合作社管理人员及其贷款人都是有价值的。然而,我们对农民合作社的信用状况和行为知之甚少,因为它们的财务报表很少公开。本文利用穆迪信用评级模型和独特的数据集,估计马尔可夫链来评估农民合作社信用质量的变化。通常估计的无条件(一刀切)概率矩阵在描述信用评级转换时被证明是不合适的。结果还显示,合作社没有表现出评级变化的势头,因为评级下调不太可能在接下来的一段时间内再次下调。净销售额低于2000万美元的农民合作社信用评级遵循平稳概率的一阶马尔可夫链,净销售额超过2.5亿美元的合作社信用评级遵循零阶马尔可夫链。本文对现有有限的农民合作社信用评级行为研究进行了补充。合作社经理、主管和贷款人可以利用这些发现做出更明智的决定,以影响未来的信用评级。
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来源期刊
CiteScore
4.40
自引率
19.00%
发文量
27
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