• Journal of Digital Convergence
• /
• v.13 no.3
• /
• pp.155-165
• /
• 2015

Bankruptcy prediction has been one of the important research topics in finance since 1960s. In Korea, it has gotten attention from researchers since IMF crisis in 1998. This study aims at proposing a novel model for better bankruptcy prediction by converging three techniques - support vector machine(SVM), fuzzy theory, and genetic algorithm(GA). Our convergence model is basically based on SVM, a classification algorithm enables to predict accurately and to avoid overfitting. It also incorporates fuzzy theory to extend the dimensions of the input variables, and GA to optimize the controlling parameters and feature subset selection. To validate the usefulness of the proposed model, we applied it to H Bank's non-external auditing companies' data. We also experimented six comparative models to validate the superiority of the proposed model. As a result, our model was found to show the best prediction accuracy among the models. Our study is expected to contribute to the relevant literature and practitioners on bankruptcy prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.5
• /
• pp.2319-2337
• /
• 2019

In this paper, we study the distributed cooperative caching for Internet content providers in a small cell of heterogeneous network (HetNet). A general framework based on bankruptcy game model is put forth for finding the optimal caching policy. In this framework, the small cell and different content providers are modeled as bankrupt company and players, respectively. By introducing strategic decisions into the bankruptcy game, we propose a caching value assessment algorithm based on analytic hierarchy process in the framework of bankruptcy game theory to optimize the caching strategy and increase cache hit ratio. Our analysis shows that resource utilization can be improved through cooperative sharing while considering content providers' satisfaction. When the cache value is measured by multiple factors, not just popularity, the cache hit rate for user access is also increased. Simulation results show that our approach can improve the cache hit rate while ensuring the fairness of the distribution.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.4
• /
• pp.246-251
• /
• 2013

In this paper, it is proposed a bandwidth allocation Scheme based on Bankruptcy theory in Distributed Mobile Multimedia Network. The proposed scheme is guaranteed a minimum allocation. So, the minimum quality of each service are guaranteed. Therefore efficient and fairness network can be configured. The performance evaluation results indicate that the proposed scheme has good performance than other existing schemes by the fairness index and the Erlang blocking formular calculation. The minimum bandwidth of the proposed scheme can be applied to other techniques of a priority based bandwidth allocation scheme and dynamic bandwidth allocation scheme.

• The KIPS Transactions:PartC
• /
• v.18C no.2
• /
• pp.97-102
• /
• 2011

Since the game theory provides a theoretical ground to distribute a shared resource between demanding users in a fair and efficient manner, it has been used for the bandwidth allocation problem in a network. However, the bandwidth allocation schemes with different game theory assign different amount of bandwidth in the same operational environments. However, only the mathematical framework is adopted when a bandwidth allocation scheme is devised without quantitatively comparing the results when they applied to the bandwidth allocation problem. Thus, in this paper, we compare the characteristics of the bandwidth allocation schemes using the bankrupt game theory and the bargaining game theory when they applied to the situation where nodes are competing for the bandwidth in a network. Based on the numerical results, we suggest the future research direction.

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

• Journal of Intelligence and Information Systems
• /
• v.20 no.4
• /
• pp.121-139
• /
• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Korea Journal of Hospital Management
• /
• v.11 no.4
• /
• pp.19-36
• /
• 2006

This study aims to examine the influence of growth rate, profitability and current ratio, which are confronted with static trade-off theory and pecking order theory, on capital structure of superior hospital and bankrupt hospital. Firstly, superior hospitals show positive correlation between growth rate and short-term loans, long-term loans, and short-term liabilities while bankrupt hospitals represent negative correlation. Superiority hospital and bankruptcy hospital show different financing behaviors, especially, short-term loan is the significant characteristic that discriminates between superior hospital and bankrupt hospital. Secondly, this paper studied the correlation between profitability and short-term loan, which the superior hospitals shows negative correlation, to contrast, bankrupt hospital have positive correlation. Consequently, the short-term loan is the most distinguishable factor between the superior hospital and bankrupt hospitals in terms of profitability. To conclude, this study shows that excess short-term loans can be the most important cause for hospital's bankrupt. Accordingly, strategic and effective policy about the short-term loan will be required in order to protect hospital's bankrupt.

• Environmental and Resource Economics Review
• /
• v.31 no.4
• /
• pp.733-753
• /
• 2022

Korean government established the Nationally Determined Contribution (NDC) in 2015. After revising in 2019, the government updated an enhanced target at the end of last year. When the NDC is addressed, the emission targets of each sector, such as power generation, industry, and buildings, are also set. This paper analyzes the emission target of each sector by applying a claims problem or bankruptcy problem developed from cooperative game theory. The five allocation rules from a claims problem are introduced and the properties of each rule are considered axiomatically. This study applies the five rules on allocating carbon emission by sector under the NDC target and compares the results with the announced government target. For the power generation sector, the government target is set lower than the emissions allocated by the five rules. On the other hand, the government target for the industry sector is higher than the results of the five rules. In other sectors, the government's targets are similar to the results of the rule that allocates emissions in proportion to each claim.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.10
• /
• pp.1-8
• /
• 2020

The objective of the study is to investigate the effect of growth opportunities on capital structure and dividend policy in Indonesia. The study employs panel data of companies listed on Indonesia Stock Exchange that distribute dividends from 2007 to 2017. Fixed and random effect regression models are used. Findings based on growth opportunities on capital structure and dividend policy in Indonesia are in line with the existing theory (i.e., contracting theory). Growth opportunities have a significant negative correlation with debt ratio and dividend yield, which suggests that firms with high growth opportunities are discouraged to generate debt to resolve underinvestment and asset-substitution problem. Firms with more investment opportunities tend to adopt a low dividend payout policy because the cash flows will be used up for investment. The positive impact of firm size on leverage is due to the low bankruptcy risk and cost of a large company. Profitability has a positive impact on the dividend policy because profitable companies can reserve larger free cash flows and, thus, pay higher dividends. The positive influence of ownership on leverage is interpreted by the unwillingness of majority stockholders to commit to equity financing in order to avoid reducing the ownership and preserve control of the company.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.10
• /
• pp.1-10
• /
• 2021

This paper attempts to investigate the determinants of capital structure of Vietnamese firms and also shed light on some of the factors of the modern theory of capital structure which is relevant for explaining the capital structure in advanced countries which are also relevant in the context of Vietnam. Using panel data from more than 1000 Vietnamese listed enterprises census 2017-2020, the paper finds that leverage ratio of Vietnamese firms is significantly related to probability. The firms have high level of fixed assets which they use as collateral, resulting in higher debt ratio, which is in line with the pecking order theory. The result also confirm that highly targeted debt ratio is positively correlated with the industry characteristics (using real estate firms as a benchmark), in which firm operates. Furthermore, consistent with the trade-off hypothesis, the leverage ratio is positively affected by non - debt tax shield. The result confirms that a large number of companies are state - owned, will have an insignificant impact of firm's size (as reverse proxy for bankruptcy cost) on leverage ratio. We also find that there is no distinction between state-owned enterprises and private enterprises due to strict adherence to the rules set by the Vietnamese government. Distinct from other countries, corporate income tax has slight impact on capital structure in Vietnamese firms.