• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.71-103
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• 2009

Considering the expenditure size of public institutions centering on public enterprises, about 28% of Korea's GDP in 2007, public institutions have significant influence on the Korean economy. However, still in the new government, there are voices of criticism about the need of constant reform on public enterprises due to their irresponsible management impeding national competitiveness. Especially, political controversy over appointment of executives such as CEOs of public enterprises has caused the distrust of the people. As one of various reform measures for public enterprises, this study analyzes the effect of internal governance structure of public enterprises on their managerial performance, since, regardless of privatization of public enterprises, improving the governance structure of public enterprises is a matter of great importance. There are only a few prior researches focusing on the governance structure and managerial performance of public enterprises compared to those of private enterprises. Most of prior researches studied the relationship between parachuting employment of CEO and managerial performance, and concluded that parachuting produces negative effect on managerial performance. However, different from the results of such researches, recent studies suggest that there is no relationship between employment type of CEOs and managerial performance in public enterprises. This study is distinguished from prior researches in view of following. First, prior researches focused on the relationship between employment type of public enterprises' CEOs and managerial performance. However, in addition to this, this study analyzes the relationship of internal auditors and managerial performance. Second, unlike prior researches studying the relationship between employment type of public corporations' CEOs and managerial performance with an emphasis on parachuting employment, this study researches impact of employment type as well as expertise of CEOs and internal auditors on managerial performance. Third, prior researchers mainly used non-financial indicators from various samples. However, this study eliminated subjectivity of researchers by analyzing public enterprises designated by the government and their financial statements, which were externally audited and inspected. In this study, regression analysis is applied in analyzing the relationship of independence and expertise of public enterprises' CEOs and internal auditors and managerial performance in the same year. Financial information from 2003 to 2007 of 24 public enterprises, which are designated by the government, and their personnel information from the board of directors are used as samples. Independence of CEOs is identified by dividing CEOs into persons from the same public enterprise and persons from other organization, and independence of internal auditors is determined by classifying them into two groups, people from academic field, economic world, and civic groups, and people from political community, government ministries, and military. Also, expertise of CEOs and internal auditors is divided into business expertise and financial expertise. As control variables, this study applied foundation year, asset size, government subsidies as a proportion to corporate earnings, and dummy variables by year. Analysis showed that there is significantly positive relationship between independence and financial expertise of internal auditors and managerial performance. In addition, although business expertise and financial expertise of CEOs were not statistically significant, they have positive relationship with managerial performance. However, unlike a general idea, independence of CEOs is not statistically significant, but it is negatively related to managerial performance. Contrary to general concerns, it seems that the impact of independence of public enterprises' CEOs on managerial performance has slightly decreased. Instead, it explains that expertise of public enterprises' CEOs and internal auditors plays more important role in managerial performance rather than their independence. Meanwhile, there are limitations in this study as follows. First, in contrast to private enterprises, public enterprises simultaneously pursue publicness and entrepreneurship. However, this study focuses on entrepreneurship, excluding considerations on publicness of public enterprises. Second, public enterprises in this study are limited to those in the central government. Accordingly, it should be carefully considered when the result of this study is applied to public enterprises in local governments. Finally, this study excludes factors related to transparency and democracy issues which are raised in appointment process of executives of public enterprises, as it may cause the issue of subjectivity of researchers.