The role of bank analysts and scores in the prediction of financial distress: Evidence from French farms

The purpose of this paper is to explore the role of bank analysts and scores in the prediction of financial distress. For regulatory and economic reasons, banks are in the frontline for assessing financial distress through “hard†and “soft†criteria. While a large literature already exists on this topic, the agricultural sector has not been investigated. However, farms are risky businesses which mainly rely on bank loans for their development. Our analysis relies on a unique dataset of 1,045 farms which are customers of a French bank. Predictors of financial distress are based on risk scores and bank analysts' opinion. Zero-inflated negative binomial and logit regressions are used to assess their explanatory power of financial distress. Results show that scores, especially the one measuring the counterparty risk, are better predictors than analysts of the occurrence of an incident and its duration. Surprisingly, the duration of the customer-bank relationship does not allow us to predict future incidents. The analysis may be extended to other sectors such as small and medium-sized enterprises.(This abstract was borrowed from another version of this item.)