- P-ISSN1738-3110
- E-ISSN2093-7717
- SCOPUS, ESCI
ISSN : 1738-3110
Performance Evaluation and Forecasting Model for Retail Institutions
Abstract
Purpose - The National Agricultural Cooperative Federation of Korea and National Fisheries Cooperative Federation of Korea have prosecuted both financial and retail businesses. As cooperatives are public institutions and receive government support, their sound management is required by the Financial Supervisory Service in Korea. This is mainly managed by CAEL, which is changed by CAMEL. However, NFFC's business section, managing the finance and retail businesses, is unified and evaluated; the CAEL model has an insufficient classification to evaluate the retail industry. First, there is discrimination power as regards CAEL. Although the retail business sector union can receive a higher rating on a CAEL model, defaults have often been reported. Therefore, a default prediction model is needed to support a CAEL model. As we have the default prediction model using a subdivision of indexes and statistical methods, it can be useful to have a prevention function through the estimation of the retail sector's default probability. Second, separating the difference between the finance and retail business sectors is necessary. Their businesses have different characteristics. Based on various management indexes that have been systematically managed by the National Fisheries Cooperative Federation of Korea, our model predicts retail default, and is better than the CAEL model in its failure prediction because it has various discriminative financial ratios reflecting the retail industry situation. Research design, data, and methodology - The model to predict retail default was presented using logistic analysis. To develop the predictive model, we use the retail financial statements of the NFCF. We consider 93 unions each year from 2006 to 2012 to select confident management indexes. We also adapted the statistical power analysis that is a t-test, logit analysis, AR (accuracy ratio), and AUROC (Area Under Receiver Operating Characteristic) analysis. Finally, through the multivariate logistic model, we show that it is excellent in its discrimination power and higher in its hit ratio for default prediction. We also evaluate its usefulness. Results - The statistical power analysis using the AR (AUROC) method on the short term model shows that the logistic model has excellent discrimination power, with 84.6%. Further, it is higher in its hit ratio for failure (prediction) of total model, at 94%, indicating that it is temporally stable and useful for evaluating the management status of retail institutions. Conclusions - This model is useful for evaluating the management status of retail union institutions. First, subdividing CAEL evaluation is required. The existing CAEL evaluation is underdeveloped, and discrimination power falls. Second, efforts to develop a varied and rational management index are continuously required. An index reflecting retail industry characteristics needs to be developed. However, extending this study will need the following. First, it will require a complementary default model reflecting size differences. Second, in the case of small and medium retail, it will need non-financial information. Therefore, it will be a hybrid default model reflecting financial and non-financial information.
- keywords
- Small Business, Default Prediction Model, Retail Institution, Logistic Regression Analysis
Reference
Ahn, Sung-Woo, Lee, Sang-Youn, Kim, Pan-Jin, & Youn, Myoung-Kil (2009). A Practical Study on the New Revenue Estimate Model Of SSM. The Journal of Distribution Science, 7(3), 5-23.
Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and The Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589-609.
Beaver, W. H. (1966). Financial Ratios As Predictors of Failure. Journal of Accounting Research, 4, 71-111.
Chang, Young-Kwang, & Kim, Yeong-Gi (2004). An Empirical Study on the Prediction of the Mutual Savings Bank Failures. Korean Journal of Money & Finance, 9(1), 1-39.
Chimucheka, T. (2013), Obstacles to Accessing Finance by Small Business Operators in the Buffalo City Metropolitan Municipality. The East Asian Journal of Business Management, 3(2), 23-29.
Chinomona, R., & Tam, L. T. (2013). Microfinance Outreach and the Microfinance Institutions(MFIs) Sustainability:Evidence from Vietnam. The East Asian Journal of Business Management, 3(1), 5-16.
Cho, Hee-Gook, & Kim, Yeong-Soo (2011). A Study on the Distress Prediction about Financial Characteristics of the Saemaeul Kumko. The Journal of Eurasian Studies, 8(2), 25-47.
Chung, Young-Woo, & Kim, Pan-Jin (2010). A Study on the Government's Support Policy of SMEs. The Journal of Distribution Science, 8(1), 33-44.
Gilbert, R. A., Meyer, A. P., & Vaughan, M. D. (2002). Could a CAMELS downgrade model improve off-site surveillance?. Federal Reserve Bank of St. Louis Review, January/February, 47-64.
Han, Du-Bong (2002). Review of Credit Evaluation on Agricultural Loans in Financial Intermediaries Serving for Agriculture. Korean Journal of Agricultural Management and Policy, 29(1), 172-189.
Jeong, Jin-Ho, & Cho, Hee-Gook (2008). A Discriminant Analysis of the Financial Characteristics of Failed Saemaeul Funds. Journal of Industrial Economics and Business, 21(5), 2015-2039.
Jeong, Ji-Man (1999). The default prediction of Credit Union and deposit insurance rate. The Journal of Social Sciences, 12, 1-22.
Kim, Hak-Kyeun, & Baek, Jae-Seung (2012). Performance Evaluation and Forecasting Model for Financial Institution: A Study on Credit Union Institutions in Korea. Korean Journal of Financial Studies, 41(1), 41-91
Kim, Ho (2012). Implications and Analyses on the Management Situation of a Consumers’Cooperative. The Korea Society of International Agriculture, 24(4), 385-391.
Kim, Tae-Kyun, Jeong, Ki-Ho, & Kim, Doo-Han (2003). Prediction of Financial Distress of Agricultural Cooperatives in Gyeongbuk Province. Korean Journal of Agricultural Economics, 44(4), 23-38.
Ohlson, J. A. (1980). Financial Ratio and Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109-131.
Paricheh, M., Mehrazeen, A., & Shiri, M. M. (2013). The Amount of Earnings Per Share’s Adjustment and Earnings Management. The International Journal of Industrial Distribution & Business, 4(1), 15-21.
Park, Ro-Kyung (1995). An Empirical Small Study on the Prediction Model of Bank Failure. Management and economics research, 18(1), 247-261.
West, R. C. (1985). A factor-analytic approach to bank condition. Journal of Banking & Finance, 9(2), 253-266.
- Downloaded
- Viewed
- 0KCI Citations
- 0WOS Citations