• 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 Labor Studies
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• v.17 no.1
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• pp.1-36
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• 2011

The purpose of this study is to analyze the effect to financial outcome and workers' outcome, using the manufacturing industry database of Human Capital corporate Panel from Korea Research Institute for Vocational Education & Training. Especially, this study used the typology of Lean production and Autonomous team production, that are the typical form of alternative work organization, to analyze. In the case of domestic manufacturing industry, individual participation practices, that have the main characteristics such as QC or suggestion system, is expanded. Therefore, with the reference of Lean production, Autonomous Team Production and the Taylor system are compared and analyzed, considering the characteristics of Socio-technical System. As a result, it is showed that the Lean production and Autonomous Team Production as a alternative work organization are more positive about the organizational performance and workers' outcome than the taylor system. However, when Lean production and Autonomous Team Production are compared, it is showed unsignificant distinction to the effect of organizational performance. Meanwhile, Lean production showed more negative effect on the every dependent variables such as working hours, income, job satisfaction, and organizational commitment as workers' performance than the Autonomous Team Production. Although the common ideas and belief is that the Lean Production is superior for the quality and organizational performance improvement, it is implied the possibility that there is some damaged workers' performance on the hidden side of that mechanism.

• Asia Marketing Journal
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• v.13 no.4
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• pp.191-212
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• 2012

With the introduction of the Capital Market Integration Act in 2009, the new competitive scope and paradigm is opened in Korean financial services market. The change of financial industry and institutions will lead to the behavioral change of customers who consume and choose financial services. While researches from the financial perspective have been conducted, works from the marketing or customer oriented approach has long been relatively ignored. The purpose of this study is to investigate influencing factors and process of financial services customers' choice behavior. More specifically, the main theme is how to enhance customer brand loyalty and purchase intention through the perception of overall quality of the service product. An integrated conceptual model including antecedents, mediating variables and consequences is established through comprehensive literature reviews of extant works on environmental change, customer behavioral change and choice behaviors. Hypothesis testing is done with SEM analysis. According to the results, the attractiveness of financial product, the reputation of financial firm, and self-brand image congruence among exogenous variables make a positive effect on perceived overall quality. And perceived overall quality has a significant effect on brand loyalty.

• The Korean Journal of Financial Management
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• v.23 no.1
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• pp.243-266
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• 2006

This paper tests cross hedging performance of the KOSPI 200 stock index futures to hedge the downside risk of the KOSPI, KOSPI 200 and KOSDAQ50 spot market. For this purpose we introduce the minimum variance hedge model, bivariate GARCH(1,1) and EGARCH(1,1) model as hedge models. The main results are as follows; First, we find that the direct hedge performance of KOSPI 200 index futures is better than those of indirect hedge performance. second, in case or cross hedge performance the hedge effect of KOSPI 200 stock index futures market against KOSPI 200 stock index spot market is relatively better than those of KOSPI 200 index futures against KOSPI and KOSDAQ spot position. Third, for the out-sample, hedging effectiveness of the risk-minimization with constant hedge ratios is higher than those of the time varying bivariate GARCH(1,1) and EGARCH(1,1) model. In conclusion, investors are encouraged to use simple risk-minimization model rather than the time varying hedge models like GARCH and EGARCH model to hedge the position of the Korean stock index cash markets.

• Journal of Navigation and Port Research
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• v.45 no.6
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• pp.276-283
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• 2021

The capital structure of the shipping business, which is characterized by its capital intensity and extreme market volatility, is closely related to long-term stability. Research in this area has been conducted mostly in the form of deriving the determinants of capital structure from company-wise financial ratios. This research, on the other hand, has a different approach to the topic. It identifies the relationship between actual cash profit and loss and other variables - i.e. actual vessel prices, interest rates and leverage ratio - by employing historical simulation. The result demonstrates that the P anamax cash profit shows 0 (break-even point) when the debt weight reaches 64.38% (debt ratio 180.74%) and the Cape, 73.04% (debt ratio 270.92%). Additionally, the ships of different types show a divided pattern for the pre- and post-'Super Boom'. It indicates that the business area and the market cycle should be considered when a leverage strategy is established. This research benefits shipping companies set a rational leverage strategy as well as delivers a reasonable guideline to government authorities for the development of a sound policy on shipping finance.

• The Journal of Society for e-Business Studies
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• v.15 no.3
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• pp.1-23
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• 2010

It is crucially important to exchange and manage the financial information of an organization for the reason of complexity and diversity of information caused by its implicit information involved. Especially, according to the development of information technology, various approaches appeared to manage financial data of organization. For example, XBRL (Extensible Business Reporting Language) is one of the technologies dealing with the above criteria. Basically, XBRL is a business reporting language to define and exchange financial information, such as a financial statement of organization. XBRL is an international standard which enables the exchange of information between information providers and consumers by adding the tags involving the information of circumstantial factors of data. However, XBRL is not able to describe semantics because XBRL is fundamentally based on XML(Extensible Markup Language) having the purpose of expressing and structuring data. Therefore, this paper aims to enable semantic information to XBRL through the semantic technology. The objective of this paper is an ontologization of the knowledge to perform sharing, reusing, discovering, and inferring the knowledge described and conducted by XBRL. In order to achieve the above objective, this paper suggests the methodology for the ontologization of the category and instance document of XBRL. Furthermore, this paper points out the possibility of suggested methodology in a practical business through indicating the advantages of the knowledge described by XBRL.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.320-332
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• 2021

This paper investigates the influence of stock-level left-tail risk, which is defined using Value-at-Risk(VaR) estimates of past one-year daily stock returns, in the expected stock returns in the Korean stock market. Our results are summarized as follows: First, monthly-constructed zero-cost portfolios that buy (shortsell) the highest (lowest) left-tail risk decile in the previous month exhibit an average monthly return (called left-tail risk premium) of -2.29%. Second, Fama-MacBeth cross-sectional regressions suggest that left-tail risk in the previous month shows significant and negative explanatory power over return in this month, after controlling for various firm characteristics such as firm size, B/M, market beta, liquidity, maximum daily return, idiosyncratic volatility, and skewness. Third, the stocks with larger recent month loss have lower returns in the next month. Fourth, the magnitude of left-tail risk premium is negatively related with lagged market-level volatility. These results support the hypothesis from a perspective of behavioral finance that the overpricing of stocks with left-tail risk is attributed to the investors' underreaction to it.

• Korean Journal of Construction Engineering and Management
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• v.14 no.3
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• pp.78-87
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• 2013

The modern society has been drastically changed from the industrial economic society to the knowledge based society, to catch up with the knowledge and the change of technology required for the modern people, the people can not live in the modern society without the continued study or education. In case of architectural design firm, it is concentrating on the productivity of enterprise by cultivating the working level through the self education focused on the improvement of inner capacity. In connection with this, the efficiency of enterprises are analyzed by carrying out the Data Envelopment Analysis(DEA) utilizing the financial ratio index in the various field of industries recently, the analysis study for the efficiency utilizing DEA is increased in the construction industries as well. However, in case of construction industries, the study focused on the efficiency of administration only has been progressed, it is the real situation that the approach for the analysis of education efficiency of each enterprise is very insufficient. Therefore, this study analyzed the education efficiency of architectural design firm after the selection of input and output variables by utilizing the DEA model and utilizing the AHP analysis technique by deducting the variables through the preceding study in relation to the education efficiency and the interview with the specialists.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.125-133
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• 2014

This paper develops a bankruptcy prediction model on an Artificial Neural Network for hotel management. A bankruptcy prediction model has a specific feature to predict a bankruptcy of the whole hotel business after evaluate bankruptcy possibility on the basis of business performance data of each branch. here are many traditional statistical models for bankruptcy prediction such as Multivariate Discriminant Analysis or Logit Analysis. However, we chose Artificial Neural Network because the method has accuracy rates of prediction better than those of other methods. We first selected 100 good enterprises and 100 bankrupt enterprises as experimental data and set up a bankruptcy prediction model by use of a tool for Artificial Neural Network, NeuroShell. The model and its experiments, which demonstrated high efficiency, can certainly provide great help in decision making in the field of hotel management and in deciding on the bankruptcy or financial solidity of each branch of serviced residence hotel.

• Journal of Korean Society for Quality Management
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• v.42 no.3
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• pp.361-372
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• 2014

Purpose: In this paper, we investigate whether the endeavors for 6 sigma quality management by a firm have positive effects on its financial performance and the length of 6 sigma implemented period affects its financial status. Methods: We conduct the analysis using the data from Workplace Panel Survey 2009. We use multiple linear regression in order to analyze the relationship between the efforts for quality management and financial performance. Specifically, the return on assets (ROA) and return on equity (ROE) are investigated as dependent variables and the efforts for quality management as independent variable. The Box-Cox transformation and Cook's distance are also used. Results: As a result of the analysis, the indication is that companies that put effect into the six sigma quality management have a positive result in its financial status. In detail, the efforts for six sigma quality management have positive effects on total asset turnover ratio and six sigma implemented period on net income to net sales ratio. Additionally, companies with longer(shorter) period of six sigma program have more (less) improvement in its financial status. Conclusion: It can be concluded that the company's efforts for quality management positively influence financial performance.