• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.1
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• pp.119-132
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• 2014

This study analyzed the failure prediction model of the firms listed on the KOSDAQ by considering whether there are embezzlement, malpractice and the largest shareholder changes or not. This study composed a total of 166 firms by using two-paired sampling method. For sample of failed firm, 83 manufacturing firms which delisted on KOSDAQ market for 4 years from 2009 to 2012 are selected. For sample of normal firm, 83 firms (with same item or same business as failed firm) that are listed on KOSDAQ market and perform normal business activities during the same period (from 2009 to 2012) are selected. This study selected 80 financial ratios for 5 years immediately preceding from delisting of sample firm above and conducted T-test to derive 19 of them which emerged for five consecutive years among significant variables and used forward selection to estimate logistic regression model. While the precedent studies only analyzed the data of three years immediately preceding the delisting, this study analyzes data of five years immediately preceding the delisting. This study is distinct from existing previous studies that it researches which significant financial characteristic influences the insolvency from the initial phase of insolvent firm with time lag and it also empirically analyzes the usefulness of data by building a firm's fail prediction model which considered embezzlement/malpractice and the largest shareholder changes as dummy variable(non-financial characteristics). The accuracy of classification of the prediction model with dummy variable appeared 95.2% in year T-1, 88.0% in year T-2, 81.3% in year T-3, 79.5% in year T-4, and 74.7% in year T-5. It increased as year of delisting approaches and showed generally higher the accuracy of classification than the results of existing previous studies. This study expects to reduce the damage of not only the firm but also investors, financial institutions and other stakeholders by finding the firm with high potential to fail in advance.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.1-15
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• 2016

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.

• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.307-314
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• 2018

It became clear that Korean peninsula is not a safe region anymore from an earthquake disaster after Gyeongju and Pohang Earthquake in 2016-2017. Unfortunately, building industry in Korea has not been well prepared for an earthquake disaster and the following problems exist. First, the rate of buildings with proper seismic performance is relatively low. Second, the number of piloti buildings which are vulnerable to earthquake and fire disaster has increased recently. Third, the proportion of small-scale buildings excluded from the application of the building law for securing safety is too high. Fourth, widespread corruption and poor construction impede safety. Therefore, measures to prepare for earthquake disaster are as follows. First, methods of reinforcing building structures and reducing the seismic load acting on a building should be utilized in order to secure the insufficient seismic performance of buildings vulnerable to earthquakes. Second, whistleblowers should be encouraged and protected to prevent defective construction due to corruption. To this end, whistleblowers should be recognized as an effective means of protecting public interest not the traitor to the organization.

• The Korean Journal of Financial Studies
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• v.10 no.1
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• pp.73-105
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• 2004

1997년 말 우리나라의 경제위기는 외화보유고의 부족에서 비롯된 것이나 근본적으로는 1996년부터 시작된 한보, 삼미, 기아그룹 등 우리나라 재벌기업의 연쇄도산으로 대변되는 기업의 실패에서 기인하였다. 지난 30년 간 빠른 성장을 지속해 온 우리나라 기업이 90년대 후반부에 총체적 부실의 위기에 직면하게 된 것은 기업외적인 요인보다도 과다차입과 과잉투자, 전근대적인 지배구조 등의 기업 내부적 요인에서 주로 기인한 것으로 지적되고 있다. 그러나 일반적으로 기업실패의 원인으로 지적되는 과다차입과 과잉투자는 문제에 대한 정밀한 진단 없이 국민정서에 부응하여 인구에 회자되는 면이 없지 않았다. 따라서 본 연구에서는 지난 40여년간 높은 부채비율을 유지하면서도 고도성장을 지속해 온 우리나라 기업이 왜 90년대 말에 와서 그것으로 인하여 도산하게 되었는지에 대한 심층적인 분석을 도모하고자 하였다. 분석 결과 우리나라 기업의 연쇄도산은 80년대 말 이래 급격하게 변화된 영업환경으로 인하여 기업의 영업위험이 커진 것이 직접적인 원인으로 작용하였으며 높은 부채비율은 총위험을 줄이지 못했다는 점에서 기업도산의 간접적인 배경을 형성한 것으로 나타났다. 또한 우리나라 기업이 지난 30여년간 투자수익이 자본비용에 못미치는 것으로 드러나 과잉투자를 해 온 것은 사실이나 과잉투자의 배경은 일반 물가상승률을 크게 상회하는 높은 지가상승에 있었으며 또한 80년대까지 정부의 성장에 대한 인센티브와도 직결되어 있었다.

• Korean Journal of Construction Engineering and Management
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• v.11 no.1
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• pp.101-112
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• 2010

The Korean real estate market currently is experiencing a slowdown due to the global economic crisis which has resulted from subprime mortgage lending practices. In response, the Korean government has enforced various policies, based on intend to deregulate real estate speculation, such as increasing the Loan to value ratio (LTV) in order to stimulate housing supply, demand and accompanying housing transactions. However, these policies have appeared to result in deep confusion in the Korean housing market. Furthermore, analyses for housing market forecasting particularly those which examine the impact of the international financial crisis on the Korean real estate market have been partial and fragmentary. Therefore, a comprehensive and systematical approach is required to analyze the real estate financial market and the causal nexus between market determining factors. Thus, with an integrated perspective and applying a system dynamics methodology, this paper proposes Korean Real Estate and Mortgage Market dynamics models based on the fundamental principles of housing markets, which are determined by supply and demand. As well, the potential effects of the Korean government's deregulation policies are considered by focusing on the main factor of these policies: the mortgage loan.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.6
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• pp.1353-1360
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• 2009

This study examines the effects of internal control system and Analysis of characteristics and Earning management in Vulnerable Cooperation. During research period 2005${\sim}$2007, Purpose of this study is to examines whether financial characteristics has exist or not. Second, Earning management also. As a result of analysis are as follows, First T-test and logistic regression has found explanatory with stastical significance about CURRENT, RNPS, and DEPT. Second, DA of Vulnerable Cooperation is significantly higher than Normal cooperation.

• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.397-409
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• 2015

The Global Financial Crisis and introduction of International Financial Reporting Standards (IFRS) urged the banks to strengthen their asset qualities. The banks dispose their non performing loans(NPLs) consistently to maintain a sufficient BIS capital adequacy ratio. Accordingly, the interests in auctions, as a disposal method, of real estates that secured for NPLs are on the increasing. This study suggest an alternative for fairness consolidation of real estate auctions which secured for NPLs. First, the impartial entry barriers for NPL sales markets need to be eliminated for fair bidding competition in auctions for real estate that secured for NPLs. In addition, the portion of NPL disposal by real estate auctions need to be expanded. Second, the asymmetry of trade information in the retail markets of NPLs and the abuse of offset by NPL owners' also should be restricted. The Fairness improvement of NPL trading process and real estate auction process that secured for them would of great use in the protection of bidders. Futhermore, it would also contribute to the revitalization of real estate auction markets and the resolution of NPLs of banks through fair disposal of distressed assets.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.89-94
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• 2023

Recently, credit risk in the Chinese corporate bond market has increased significantly, and there is a possibility that banks that have invested in corporate bonds may become insolvent. The purpose of this study is to empirically analyze the effect of Chinese commercial banks' investment in securities on financial performance. The analysis results are as follows. First, it is estimated that as the share of securities investment by Chinese commercial banks increases, the bank's profitability decreases. It was found that investment in securities did not have a positive impact on profitability due to the increase in credit risk in the corporate bond market and the increase in marginal companies. Second, it is estimated that as the proportion of securities investment by Chinese commercial banks increases, the bank's soundness deteriorates. As credit risk in China's capital market is increasing, continuous management of non-performing assets is required. Chinese commercial banks need portfolio management through securities investment in addition to loan assets to improve profitability. However, volatility should be managed by adjusting the scale of securities management to an appropriate level.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.187-195
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• 2010

So that this study confirms going concern's availability laying focus on entertainment industry possibility of default judge, focus in cash flow that is important accounting indicator pointer to do cause of bankruptcy, payable capability, insolvent estimate etc and analyzed effect that governance gets in cash flow. The sampling period was from 2005 to 2008 and the number of samples was 44. In analysis technique, implement basic statistical, t-test, correlation, regression. Is as following if summarize result. CFO, for debt ratio, negative(-), enterprise size was exerting positive(+), and cash flow by investment activity enterprise size negative(-) influence reach.