• The Korean Journal of Franchise Management
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• v.5 no.1
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• pp.85-108
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• 2014

This study provides the information about size, financial structure, profitability and growth of franchisors using financial data(asset, liability, equity, sales volume, operating income and net income) in uniform franchise offering circular of fair trade commission. The data were collected from 1,050 franchisors in various business fields: fast food, family restaurant, bakery, agriculture & fishery and liquor shop in the uniform franchise offering circular in 2012 and 2011. Results of this study are as follows: For company size, median of total assets was KRW 675 million and the accumulated median assets rate was 0.48%, but the accumulated median company numbers were 49.9%, which showed small size. For financial structure, 525 companies were below 200% debt ratio, while 314 (29.9%) companies were in over 200% debt, and 211 (20.1%) companies were impaired in capital. These also showed financial structure was vulunerable. For profitability, median of ROA for total companies were only 4.72%, which showed low profitability. For growth, median of growth rate for sales were 7.57% per year, which showed mature industry. In overall, the results showed franchisors should improve their financial status.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.1-15
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• 2016

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.