• 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.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of Distribution Science
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• v.12 no.6
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• pp.81-88
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• 2014

Purpose - Entrepreneurship promotion is emerging as an important economic growth agenda. However, in Korea, entrepreneurship has weakened because of the collapse of the venture bubbles of the 2000s and the global economic recession in 2008, which have induced the business community to choose stability over risk. The Korean government has been implementing several support projects to inspire and promote youth entrepreneurship through various means including financial assistance; however, the perpetuation rate of young entrepreneurship is still low as compared to advanced economies such as the US and EU. This case study focuses on the Youth Start-Up Business Support Program of the Small & Medium Business Corporation, and explores practical alternatives. Further, it aims to suggest managerial factors and a conceptual model for change management factors affecting the business performance creation of a startup company, based on the Small and medium Business Corporation's young venture startup fund. Research design, data, and methodology - Many studies examine the current progress and issues of startup firms, for example, a lack of systematic cultivation of entrepreneurship and startup business training, lack of commercialization funding for youth startup businesses, lack of mentoring, and inadequate infrastructure. From prior research, we address four factors, namely, personal managerial capabilities, innovative business model, sufficient cash flow, and social network, affecting startup companies' business performance. This study involved a sample survey of 200 young entrepreneurs to investigate casual relations between the four factors and business performance. A regression analysis was used to verify the hypotheses. Results - First, in relation to differences in the founder's personal characteristics, age, sales amount, and number of employees significantly impact business performance. Second, regarding the causal relation between the four factors for creating business performance, an innovative business model and social networking have supported the hypotheses, revealing that the more that a start-up founder has an innovative business model and social networking, the more the start-up firms are likely to have better performance (e.g., sales volume, employment, ROE, ROI, etc.). Although the founder's competency and sufficient cash flow have no significant relationship with business performance, the mean value was higher performance for high founder's competency and sufficient cash flow. Conclusions - This study provides basic data on policy support strategies of the Small and Medium Business Corporation, to help young entrepreneurs achieve their start-up business goals. It shows that young entrepreneurship startup firms should strive to explore ideas to satisfy customers' needs, and that changes in customer value and the continuous innovation of business model differentiation are required to actively respond to change management. Moreover, at the infant startup stage, they should activate social network programs to share information, thereby offsetting resource scarcity and managing business risk. Further, the establishment of a long-term vision and the implementation of training programs in related specific fields should be supported to strengthen founders' personal capabilities.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.1 no.2
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• pp.157-192
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• 2006

Since the late 1990s, the Korean Venture Capital Industry has been remarkably grown in the aspect of quality and quantity. Korean government expects that the Venture company and Venture Capital Industry would contribute to the recovery of depressed Korean economy and restructuring of the high cost and low efficiency economic structure. Korean government reinforces supporting policies for the Venture Capital and Venture Business. Venture Capital is defined as the form of high risk and high profit investment capital growing the small & medium enterprises to competitive ones through capital and management support and collecting the capital. According to the Gompers and Lerners the venture capital cycle consists of raising investment capital, screening the investment opportunity and invest the money. And later, sold the retained stock to the other investor or to the company. This stage called EXIT Consequently, the function of the venture capital, which supply the fund and the business consultation to venture business, have been emphasized and how to effectively run this capital have been recognized as the way to develop the venture business. In this regard, the problem in Korean Venture Capital Market is as follows. First, most of the sources of fund depends on the government support and this conflict with the nature of risk capital because the government capital emphasis the stability than profitability. And secondly, the efficiency of the venture capital system in Korea do not reach that of the advanced countries due to many kinds of restriction and the rack of support. Consequently, the Activation Schemes for Korean Venture Capital Firms are as follows. First, the sources of venture capital need to diversify from angels to institutional investors such as banks, pensions, fund of fund. And Lastly, the internal management and operational system of venture capital companies should be strengthened by quality to that of global Venture Capital Firms.

• Journal of Technology Innovation
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• v.18 no.2
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• pp.147-174
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• 2010

This paper aims to analysis sectoral innovation patterns of technological innovation and localized knowledge spillover in Korean manufacturing sector. Sectoral innovation system approach proposed that the specific pattern of innovative activity and knowledge spillover in an industry can be explained as the outcome of different technological regimes. Technological regime is defined by the particular combination of technological opportunities, appropriability of innovations, cumulativeness of technical advances and properties of the knowledge base. Based on a sample of 2,882 firms in manufacturing sector, this paper provides empirical estimates of the relationships between firm's product innovation and localized knowledge spillover. Results of the analysis provide considerable support to the hypothesis that firm's product innovation and localized knowledge spillover are related to the nature of the underlying technological regime. In the industry based on the tacit and specific knowledge, firm's product innovation is positively related to the localized knowledge spillover. This paper also shows that high stability in the ranking of innovators are related to high degrees of cumulativeness and appropriability.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.33-51
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• 2022

This study analyzes newly listed companies on KOSDAQ from 2011 to 2020 for both firms having experience in attracting venture investment before listing (VI) and those without having experience in attracting venture investment (NVI) by examining differences between two groups (VI and NVI) with respect to both the level of listing performance and that of firm performance (growth) after the listing. This paper conducts descriptive statistics, mean difference, and multiple regression analysis. Independent variables for regression models include VC investment, firm age at the time of listing, firm type, firm location, firm size, the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company. Throughout this paper, results suggest that listing performance and post-listed growth are better for VI than NVI. VC investment shows a negative effect on the listing period and a positive effect on the sales growth rate. Also, the amount of VC investment has negative effects on the listing period and positive effects on the market capitalization at the time of IPO and on sales growth among growth indicators. Our evidence also implies a significantly positive effect on growth after listing for firms which belong to R&D specialized industries. In addition, it is statistically significant for several years that the firm age has a positive effect on the market capitalization growth rate. This shows that market seems to put the utmost importance on a long-term stability of management capability. Finally, among the VC characteristics such as the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company, we point out that a higher market capitalization tends to be observed at the time of IPO when the level of expertise of anchor VC is high. Our paper differs from prior research in that we reexamine the venture ecosystem under the outbreak of coronavirus disease 2019 which stimulates the degradation of the business environment. In addition, we introduce more effective variables such as VC investment amount when examining the effect of firm type. It enables us to indirectly evaluate the validity of technology exception policy. Although our findings suggest that related policies such as the technology special listing system or the injection of funds into the venture ecosystem are still helpful, those related systems should be updated in a more timely fashion in order to support growth power of firms due to the rapid technological development. Furthermore, industry specialization is essential to achieve regional development, and the growth of the recovery market is also urgent.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.325-342
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• 2018

The Korea Emissions Trading Scheme ( K-ETS), which manages roughly 70% of the greenhouse gas emissions in South Korea, was initiated in 2015, after implementation of its 1st basic plan and the 1st allocation plan (2014) for the 1st phase (2015-2017). During the three and a half years since the launch of K-ETS, there have been critical policy change such as adjustment of the institutions involved, development and revision of the 2030 national GHG reduction roadmap, and change in the allocation plans. Moreover, lack of liquidity and fluctuation of carbon prices in the K-ETS market during this period has forced the Korean government to adjust the flexibility mechanism and auction permits of the market stability reserve. To evaluate the policy change in the K-ETS regarding conformance to its objectives, this study defines three objectives (Environmental Effectiveness, Cost Effectiveness and Economic Efficiency) and ten indicators. Evaluation of Environmental Effectiveness of K-ETS suggests that the national GHG reduction roadmap, coverage of GHG emitters and credibility of MRV positively affect GHG mitigation. However, there was a negative policy change implemented in 2017 that weakened the emission cap during the 1st phase. In terms of the Cost Effectiveness, the K-ETS policies related to market management and flexibility mechanism (e.g. banking, borrowing and offsets) were improved to deal with the liquidity shortage and permit price increase, which were caused by policy uncertainty and conservative behavior of firms during 2016-2018. Regarding Economic Efficiency, K-ETS expands benchmark?based allocation and began auction-based allocation; nevertheless, free allocation is being applied to sectors with high carbon leakage risk during the 2nd phase (2018-2020). As a result, it is worth evaluating the K-ETS policies that have been developed with respect to the three main objectives of ETS, considering the trial?and?error approach that has been followed since 2015. This study suggests that K-ETS policy should be modified to strengthen the emission cap, stabilize the market, expand auction-based allocation and build K-ETS specified funds during the 3rd phase (2021-2025).

• Journal of the Economic Geographical Society of Korea
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• v.20 no.3
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• pp.329-352
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• 2017

Since the late 2000s Korean foreign direct investors in North Korea and China border regions have gone through the closure of outward processing trade(OPT) networks and changes in their location due to UN security council resolution and Korean independent sanctions against North Korea's nuclear and missile tests. However, the introduction of new Chinese OPT policy has led to the invigoration of domestic market-based OPT networks towards North Korea. The main aim of this paper is to identify the exceptional characteristics of Dandong in Liaoning province, a North Korea and China border region by analyzing OPT networks towards North Korea. Fundamentally the establishment of OPT networks towards North Korea is likely to be based on the utilization of a plenty of low wages in North Korea. The main reasons for this are fallen into two perspectives: geo-economics and geo-politics. The first perspective is geo-economics centering on the consolidation of economic exchange between North Korea and China, and North Korean economic development. For example, the introduction of Chinese OPT in border region has enabled Chinese local firms based on domestic market to access a plenty of low wage in North Korea in formal and institutional contexts. The second is geo-politics for the stability of North Korean regime based on the means of geo-economics. As the invigoration of domestic market-based OPT networks might make North Korea possible promoting foreign money earning, it enable North Korea to be sustainable as a buffering region between capitalist and socialist regime for China. It shows Chinese geo-strategic attempts to deal with the economic and regime stability of North Korean as a buffering state. In other words, OPT networks in North Korea should be concerned with the discourse practice of geo-economics and geo-politics which might lead to various and contingent spatial economies in border region. As a consequence, North Korea and China border regions could defined as a space in which is applicable to exceptional institutions and policies, and an exploitative space in which create surplus and rents by utilizing a plenty of low wages in North Korea through OPT networks.

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