Credit risk migration analysis of farm businesses

Barry, Peter J.; Escalante, Cesar L.; Ellinger, Paul N.

doi:10.1108/00214840280001125

Cited by 24 publications

(19 citation statements)

References 7 publications

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“…While Barry et al (2002) did find credit scoring systems based on the historic rates of movement of individual loans was stable over longer periods, clearly farmers who have made regular contributions to repayment are a reasonable risk. In reality, this measure must reflect some of the farmer’s personal characteristics which are relatively simple to determine using the question sets, or similar, used in the survey (Greig et al , 2017).…”

Section: Discussionmentioning

confidence: 98%

“…Zech and Pederson (2003) found the success in using historic criteria in predicting repayment varied considerably in different years. Similarly, Barry et al (2002) used historic data to assess credit risk migration over various time periods. Financial, production and price conditions, particularly for farm businesses working on the world stage, constantly change.…”

Section: Introductionmentioning

confidence: 99%

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Farmers’ characteristics’ and the propensity to reduce debt

Greig

Nuthall

Old

2019

AFR

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Purpose The purpose of this paper is to investigate a farm manager’s personal characteristics (personality, age, education, objectives, experience, etc.) as drivers of debt payback success and rates. Traditionally bankers have used historic business statistics, and equity levels, to assess loans and credit worthiness. It is hypothesised that a managers’ personal characteristics are likely to be a better predictor of future debt payback performance. Design/methodology/approach The literature was searched to isolate the managers’ personal variables likely to determine debt payback. The information led to defining a quantitative model based on the theory of planned behaviour (TPB) which was hypothesised as determining payback rates where a choice was available. A postal random stratified survey of NZ owner operator farm managers provided the data to test the model and define its parameters using regressions, structural equation modelling and statistical comparisons. Findings The modelling results make it clear a manager’s personal characteristics are highly correlated with debt payback and, logically, are very likely to be the drivers. Four random effects equations and a comparison of high- and low-debt payback managers led to this conclusion. Practical implications Bankers should use the managers’ personal characteristics, as defined in the regressions, alongside traditional measures when assessing farm business loan requests. This approach is opposite to the traditional methods using mainly historic data. Originality/value The use of the TPB in assessing debt payback is a new and novel approach showing how enduring personal characteristics can be used in assessing proposals, and particularly, entrepreneurs’ adventurist investments in situations where historic data are not available.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Farmers’ characteristics’ and the propensity to reduce debt

Greig

Nuthall

Old

2019

AFR

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“…The discussion focuses on the implications of credit quality shifts, as depicted in the corresponding credit grading (borrower upgrades/downgrades), inducing respective credit line financing adjustments in the bank loan portfolio (Barry, Escalante, and Ellinger 2002). (Although the issue of credit rating transition probabilities is not considered at this stage, this remains a challenging empirical exercise for future research.)…”

Section: Implications Of Credit Rating Shifts For Bank Loan Facilitiesmentioning

confidence: 99%

An integrated credit rating and loan quality model: application to bank shipping finance

Gavalas

Syriopoulos

2014

Maritime Policy & Management

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“…Several works (Barry et al, 2002;Escalante et al, 2004;Featherstone et al, 2006Featherstone et al, , 2007Durguner and Katchova, 2011;Jouault and Featherstone, 2011) have examined and established models to assess risk in agricultural lending. Barry et al (2002) estimated migration rates using the performance measures of credit score, rate of return on equity, and repayment capacity for Illinois farmers using farm-level data. They concluded the migration concept can be used for other financial performance measures similar to how it is used for traditional default rates.…”

Section: Introductionmentioning

confidence: 99%

Factors affecting risk-rating migration

Featherstone

Wilson

Zollinger

2017

AFR

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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.

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Credit risk migration analysis of farm businesses

Cited by 24 publications

References 7 publications

Farmers’ characteristics’ and the propensity to reduce debt

Farmers’ characteristics’ and the propensity to reduce debt

An integrated credit rating and loan quality model: application to bank shipping finance

Factors affecting risk-rating migration

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