Factors affecting risk-rating migration

Purpose - The purpose of this paper is to examine empirical customer account data from 2006 through 2012 to review the probability of default (PD) rating methodology implemented by a FCS association for production agricultural accounts. This analysis provides insight into the migration of accounts across the association’s currently established PD rating categories with negative migration being a precursor to potential loan default. Design/methodology/approach - The data set contained 17,943 observations from the years 2006 to 2012 and consisted of various fields of data including balance sheet date, earnings statement date, and PD rating as of the statement date. The methods include analysis on the dynamics of the PD ratings and component ratios. OLS regression was used to analyze the data to see how the current period PD rating and component ratios affected the PD rating one year, three years, and five years out. OLS regression examined the statistical significance of the PD ratings and ratio components for this analysis. The dependent variable,Future PD Rating, represents the assigned PD rating for the observed farm either one, three, or five years into the future. It is expected that the initial PD rating in any given year would have a positive relationship, and be statistically significant in estimating future PD ratings. The independent variables are the current PD rating and the various component ratios of the inverse current ratio (CR), the debt to asset ratio (D/A), the gross profit to total liabilities ratio, the inverse debt coverage ratio, working capital to gross profit, and funded debt to EBITDA. Findings - Results indicate that financial ratio information gathered today can do a good job forecasting PD ratings up to three years in the future. CR information does not forecast five years into the future very well. Thus, there is an important need to update financial information on a regular basis. The results indicate that the D/A information is very important in predicting risk ratings. As the production agriculture sector has experienced difficult financial conditions during 2014 and 2015, agricultural finance institutions need to obtain up-to-date financial information from their clientele to effectively assess the risk of and manage their financial portfolio. Originality/value - Several previous works have examined and established models to assess risk in agricultural lending. This research adds to this body of work by examining the migration of an account’s risk-rating class over time using actual lender account data.