• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Korean Journal of Social Welfare
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• v.53
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• pp.129-153
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• 2003

This study examines the socio-economic impacts of recent welfare reform in the United States. Based on the neo-conservative critique to the traditional public assistance system for low-income families, the 1996 welfare reform has given greater emphases on reducing welfare dependency and increasing work effort and self-sufficiency among welfare recipients. In particular, the welfare reform legislation instituted 60-month lifetime limits on cash assistance, expanded mandatory work requirements, and placed financial penalties for noncompliance. With the well-timed economic boom in the second half of the 1990s, the welfare reform seems to achieve considerable progress; welfare caseload has declined sharply to reach less than 50% of its 1994 peak, single mothers' labor force participation has increased substantially, and child poverty has decreased. In spite of these good signals, the welfare reform also has several potential problems. Many welfare leavers participate in the labor market, but not all (or most) of them. The economic well being of working welfare leavers did not increased significantly, because earnings increase was canceled out by parallel decrease in welfare benefits. Furthermore, most of working welfare leavers are employed in jobs with poor employment stability and low wages, making them highly vulnerable to frequent layoff, long-time joblessness, persistent poverty, and welfare recidivism. Another serious problem of the welfare reform is that a substantial number of welfare recipients are faced with extreme difficulties in finding jobs, because they have severe barriers to employment. The new welfare system with 5-year time limit can severely threaten the livelihoods of these people. The welfare reform presupposes that welfare recipients can achieve self-reliance by increasing their labor market activities. However, empirical evidences suggest that many people are unable to respond to the new, work-oriented welfare strategy. It may be a very difficult task to achieve both objectives of the welfare reform((1) providing adequate income security for low-income families and (2) promoting self-sufficiency) at the same time, because sometimes they are conflicting each other. With this in mind, a possible solution can be to distinguish welfare recipients into "(Very)-Hard-to-Employ" group and "(Relatively)-Ready-to-Work" group, based on elaborate examinations of a wide range of personal conditions. For the former group, the primary objective of welfare policies should be the first one(providing income security). For the "Ready-to-Work" group, follow-up services to promote job retention and advancement, as well as skill-training and job-search services, are very important. The U. S. experiences of the welfare reform provide some useful implications for newly developing Korean public assistance policies for the able-bodied low-income population.