The Financial Crisis and the Bankruptcy of Small and Medium Sized-Firms in the Emerging Market

Transcription

1 The Financial Crisis and the Bankruptcy of Small and Medium Sized-Firms in the Emerging Market Sung-Chang Jung, Chonnam National University, South Korea Timothy H. Lee, Equifax Decision Solutions, Georgia, U.S.A Key Words Financial Crisis, Bankruptcy, Credit Scoring Model, Economic Condition Abstract The purpose of this paper is to investigate how the financial crisis in the emerging market can be related with the bankruptcy of small and medium firms. It is argued that there need to be risk forecasting tools which incorporate economic recession and would dynamically adjust for economic changes. Most previous studies have been interested in the credit assessment for large listed companies. However, there is a paucity of research reporting on the performance of commercial credit scoring application for small and medium firms incorporating economic conditions such as financial crisis. With the data of Korean small and medium bankrupt firms before and after the financial crisis, we expect to show that the structural changes of financial market are related with the bankruptcy of small and medium firms in the emerging market. The result in Korea was found to be different from what previous researchers expected. I. Introduction The purpose of this paper is to investigate how the financial crisis in the emerging market can be related with the bankruptcy of small and medium firms. It is argued that there need to be risk forecasting tools which incorporate economic recession and would dynamically adjust for economic changes. Most previous studies have been interested in the credit assessment for large listed companies. However, there is a paucity of research reporting on the performance of commercial credit scoring application for small and medium firms incorporating economic conditions such as financial crisis. The Asian Financial Crisis of is a good experimental environment for analyzing the effect of the structural changes in an economy on the credit scoring strategies of lending institutions. The financial crisis affected most of the emerging countries economic structures, and firms financial policies and financial institutions credit policies in those countries have since changed. For example, after IMF bailout of 1997, the Korean government strongly suggested that listed firms in the securities market lower the debt ratio below 200%, hostile take-over other corporation is possible in the financial market through tender offering the stocks, and the accounting rules have been changed into the international standards. In this study, we tried to show how the drastic change of economic conditions such as the financial crisis affects credit risk assessing tools by using South Korean business bankruptcy data. Our hope is that the incorporation of economic conditions will motivate some lending institutions to develop their own models for small and medium firms by considering economic variables, as well as the financial characteristics of the firms. The criteria for granting the credit for the small and medium firms should be different from those for large listed firms, since their financing sources and investment strategies are different. II. Data With the data of Korean small and medium bankrupt firms before and after the financial crisis, we expect to show that the structural changes of financial market are related with the bankruptcy of small and medium firms in the emerging market. The sampled population for bankrupt firms was drawn from the data base of N Korean financial institutions. The total sample covers the period of 1995 to 2000, which spans 3 years before and after the financial crisis. We divided the total sample into two groups. The first group includes the firms that went bankrupt before the financial crisis, (i.e. Nov. 1997). The second group has the firms that went bankrupt after Nov The good firms for the control sample were those that did not go bankrupt by 1997 for the first group ( before the financial crisis ) and by 2000 for the second group ( after the financial crisis ), and filed the financial statements for the last 3 years. The matching good firms belonged to the same industry as the bankrupt firms, and had similar asset sizes to the bankrupt firms. The sampled firms are selected from those that have sales volumes and total assets of higher than 1729

2 1billlion Korean won (approximately equivalent to $ 833,000 US). The total number of good firms and bankrupt firms used in this analysis are shown by year and industry in the following table 1. The total number of sample firms is 1334: the first group (the bankrupt firms before the financial crisis) consists of 667 firms; and the second group (the bankrupt firms after the financial crisis ) also has the same number of firms. <Table 1> Samples before and after the financial crisis to be completed later- Before the financial crisis After the financial crisis Industry Total Number of good firms Number of bankrupt firms Manufacturing Construction Wholesale and Retail trade Sub-total Manufacturing Construction Wholesale and Retail trade Sub-total The financial variables of the firms used in this study are 11 financial ratios, which were selected from 34 financial ratios. We had t-tests for each of 34 variables to check the significance of each variables for predicting credit quality, and selected the final 11 variables for the logistic model with the consideration of the multi-collinearity problems. There are many financial ratios which have been used for forecasting the firms bankrupt. The some previous studies (Wolkenfeld [1998], Mills and Yamamura [1998]) found the financial ratios related to cash flow most significant in estimating the probability of bankruptcy. The 34 financial variables include cash flow ratios, profitability ratios, productivity ratios, growth ratios, Moody s ratios, activity ratios and stability ratios etc. The dummy variable for the financial crisis is used: 0 for the period of Jan to Oct.1997 (before the financial crisis), and 1 for the period of Nov to Dec (after the financial crisis). The financial crisis of Nov is regarded as the most significant event to change the structure of the Korean financial market and macro-economy. The data glossary for the financial variables and economic conditions is as follows: Variables Description Y: Performance: Good Firms = 1 Bankrupt Firms = 0 X1: Stockholder's equity to total assets = ( Stockholder's equity / total asset)*100 X2: Financial expense to sales = (financial expense/sales)*100 X3: Ordinary income to total capital = (ordinary income/ total capital) *100 X4: Total asset turnover = (sales/total asset) X5: Payables turnover = sales / payables X6: Ratio of value added to sales = (value added/sales)*100 X7: Sales growth rate =(sales of the year/ sales of previous year-1)*100 X8: Cash flow ratio = cash flow after interest / sales X9: Return on Investment = earning before tax / (equity + borrowings of long-term and short term borrowings + debentures) 1730

3 X10: Stockholder s equity Ratio = Stockholder sequity /(Stockholder s equity + Borrowings of long-term and short term borrowings + debentures) X11: Sales Dummy: 0 for the period of Jan to Oct. 1997, and 1 for the period of Nov to Dec III. Methodology: Logistic Regression Logistic Regression provides the likelihood of being in one group for a firm in a two group classification. For example, the likelihood of one being in a creditworthy group for a firm's given characteristic profile values. Let E be the event that a firm is from creditworthy group and x the firm's characteristic profile vector. Then, the score generated from logistic regression is Φ(x) Prob.(E x) = 1/{ 1 + exp(-βx)}, where β is the unknown parameter vector that is to be estimated from the sample IV. Empirical Findings 1. Basic Statistics Basic statistics of the financial variables for bankrupt firms and good firms are shown in table 2. All the financial ratios of the good firms are significantly different from the bankrupt firms. The null hypothesis that the mean of each financial ratio is equal is thus rejected as statistically significant. Table 3 shows how the financial variables of good firms were affected by the financial crisis. As shown in table 3, the changes of means of X6 (ratio of value added to sales) and X7 (sales growth rate) are statistically significant at the 1% and 5% level. The ratio of value added to sales and sales growth rate became smaller after the IMF bailout. The means of the other variables had not been changed statistically significant. These results show that macro-economic recession affects the ratio of value added to sales and sales growth rate. In contrast to the good firms, the financial variables of the bankrupt firms show significant change in many financial characteristics before and after the recession. As shown in table 4, X2 (financial expense to sales) and X11 (sales) increased significantly, while X3 (ordinary income to total capital), X4(total asset turnover), X7 (sales growth rate), X9 (return on investment), X10 (stockholder s equity ratio) decreased significantly. These results imply that there needs to be risk forecasting tools which incorporate economic recession, and would dynamically adjust for economic changes. These results are quite different from those which Jung and Lee (2001) showed for the listed big companies. In the case of listed firms, the financial crisis does not affect the financial variables of the bankrupt firms significantly, though it does affect the financial characteristics of the good firms significantly. <Table 2> Basic Statistics of Financial Variables for Bankrupt Firms and Good Firms. Bankrupt Firm (N=1334) Good Firm (N=1334) t-statistics MEAN STDV MEAN STDV X *** X *** X *** X *** X *** X *** X *** X ** X *** X *** X *** 1731

4 <Table 3> Basis Statistics of Financial Variables for Good Firms Before and After the Financial Crisis Before the financial crisis After the financial crisis t-statistics MEAN STDV MEAN STDV X X X X X X *** X ** X X X X <Table 4> Basis Statistics of Financial Variables for Bankrupt Firms Before and After the Financial Crisis Before the financial crisis After the financial crisis t-statistics MEAN STDV MEAN STDV X X *** X *** X *** X X X *** X X *** X ** X ** 2. The Effects of Macroeconomic Conditions on Bankrupt Probability In this section, we analyzed the effect of structural changes in the economy on the forecasting power of bankruptcy by logistic analysis. For the proxy variable of economic condition, a dummy variable for the period of financial crisis is used. The financial crisis of Nov affects most of economic structures drastically in Korea. Table 5 shows the estimates of the coefficients of each explanatory variable in the logistic analysis. The X1 through X11 are statistically significant and the signs of the coefficients are the same as the univariate t-test showed. The dummy, however, is not significant at the 10% level. It implies that the economic recession in Korea is not directly related to the forecasting of the bankruptcy for each firm, though the financial characteristics of bankrupt small and medium sized firms in an economy had been changed significantly. 1732

5 <Table 5> Result of Logistic Analysis Estimate Chi-Square p-value Intercept X <.0001 X X X <.0001 X X X X X X X <.0001 DUMMY V. Discussion This study investigated the effect of macroeconomic conditions on the forecasting power of bankruptcy. Since Korea experienced the serious financial crisis of November, 1997, the cases with Korean small and medium sized firms around the financial crisis are good experimental samples to see how bankruptcy is related to macro-economic recession. In order to check how economic conditions may be used to forecast bankruptcy, this study compared the financial characteristics of good firms and bad firms before and after the financial crisis. Just 2 financial variables out of 11 financial characteristics for the good firms have changed significantly after the financial crisis. By contrast, the most of variables for the bankrupt firms changed significantly. These results are quite contrasted with the case of listed big firms: the financial crisis does not affect the financial variables of the bankrupt listed firms significantly, though it does affect the financial characteristics of the good listed firms significantly. It implies that small and medium sized bankrupt firms have adjusted themselves to new economic changes quickly. In the last stage, this study tried to analyze the effect of macroeconomic variables on the forecasting of bankruptcy by logistic analysis, but found that the dummy variables for the financial crisis is not significant in the logistic analysis. These results are different from what previous research has argued that there is a need to incorporate the macroeconomic recession to forecast the bankruptcy of firms. We conjecture that economic recession is significantly related to the financial characteristics for the bankrupt firms, but not directly related to the forecasting an individual firm s probability of bankruptcy. References Altman, E.I. (1968), Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankrupty, Journal of Finance, Vol. 23, No.4, pp Altman, E.I. (1994), Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Network: The Italian Experience, Journal of Banking and Finance, 18, pp Beaver, William H. (1966), Financial Ratios as Predictors of Failure, Journal of Accounting Research, Vol 4, pp Coats P.K. and Fant L.F. (1993), Recognizing Financial Distress Patterns Using a Neural Network Tool, Financial Management, Autumn, pp Crook, J. N., Hamilton, R., and Thomas, L. C. (1992), The Degradation of the Scorecard over the Business Cycle, IMA Journal of Mathematics Applied in Business and Industry 4, pp

6 Fisher, R. A. (1936), The Statistical Utilization of Multiple Measurements, Annals of Eugenics, Jung, Sung-Chang, and Lee, Timoth H., The Change of Economic Conditions and Commercial Credit Scoring Applications, Proceedings of the Annual Meeting of the American Statistical Association, August 5-9, Lacher R.C., Coats P.K., Sharma S and Fant L.F. (1995), A Neural Network for Classifying the Financial Health of a Firm, European Journal of Operational Research 85, pp Mills, John R and Jeanne H. Yamamura (1998), The Power of Cash Flow Ratios, Journal of Accountancy, October, pp Parker, D. B. (1985), "Learning-logic", Report TR- 47, Cambridge, MA: Massachusetts Institute of Technology, Center for Computational Research in Economics and Management Science. Rumehart, D. E., McClelland, J. L. (1986), "Parallel Distributed Processing: Explorations in the Microstructure of Cognition", Vol. 1, Foundations. MIT Press. Zandi, M. (1998), Incorporating Economic Information into Credit Risk Underwriting, In: Mays, E. (Ed.), Credit Risk Modeling, Glenlake Publishing, Chicago, pp Wolkenfeld, Suzanne. (1998), CFAR: A New Tool for Predicting Credit Quality, Journal of Applied Corporate Finance, Spring 1998, pp