• The Journal of Industrial Distribution & Business
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• v.10 no.6
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• pp.51-58
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• 2019

Purpose - Prior studies reported that the opacity of information caused stock price crash. If managers fail to disclose unfavorable information about the firm over a long period of time, the stock price is overvalued compared to its original value. If the accumulated information reaches a critical point and spreads quickly to the market, the stock price plunges. Information management by management's disclosure policy can cause information uncertainty, which will lead to a plunge in stock prices in the future. Thus, this study aims at examining the impact of disclosure quality on crash risk by focusing on the unfaithful disclosure firms. Research design, data, and methodology - This study covers firms listed on KOSPI and KOSDAQ from 2004 to 2013. Firms excluded from the sample are non-December firms, capital-eroding firms, and financial firms. The financial data used in the research was extracted from the KIS-Value and TS2000 database. Unfaithful disclosure firm designation data was collected from the Korea Exchange's electronic disclosure system (kind.krx.co.kr). Stock crash is measured as a dummy variable that equals one if a firm experiences at least one crash week over the fiscal year, and zero otherwise. Results - Empirical results as to the relation between unfaithful disclosure corporation designation and stock price crashes are as follows: There was a significant positive association between unfaithful disclosure corporation designation and stock price crash. This result supports the hypothesis that firms that have previously exhibited unfaithful disclosure behavior are more likely to suffer stock price plunges due to information asymmetry. Second, stock price crashes due to unfaithful disclosures are more likely to occur in Chaebol firms. Conclusions - While previous studies used estimates as a proxy for information opacity, this study used an objective measure such as unfaithful disclosure corporation designation. The designation by Korea Exchange is an objective evidence that the firm attempted to conceal and distort information in the previous year. The results of this study suggest that capital market investors need to investigate firms' disclosure behaviors.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.268-280
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• 2021

This study analysed the narrative strategies in TV drama utilized in order to support the recent feminism movements. The analysis revealed that this TV drama breaks away from the clichéd patriarchal drama series. It portrays the main characters are not the sons but the two daughters-in-law, and represents the women challenging the order of the patriarchy, and resolving the issues. In this drama, men's power was removed and female agents were held up to ridicule. In addition, it eradicates the traditional female conflict structures and creates a strong bond between the females. With this storyline, TV series concludes with two achievements. One, the stepmother and the mother co-parent the child instead of the father, suggests that a non-blood related matriarchal family is possible. Two, the heir to the chaebol family, which is traditionally a patrilineal structure, is not the oldest son or the immoral son, but the lesbian daughter-in-law, overturning the idea of heteronormativity that is dominant in the patriarchal system.

• Management & Information Systems Review
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• v.36 no.4
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• pp.53-80
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• 2017

The purpose of this study is to examine the determinants of the number of bank relationships in Korea. Firm-specific determinants considered here include leverage, size, age, return on asset, investment grade, tangibility, liquidity, R&D expenditure. We estimate the effects of these variables, and compare the results with those from previous studies performed for other economies. Concerning the effects of business cycle, we find that the business cycle is an important factor in determining the number of bank relationships. The number of bank relationships varies over the business cycle, and we notice a counter-cyclical behavior, which means the number decreases during economic expansions and increases during contractions. This result can be interpreted as a result of firms' diversification of borrowings into multiple banks in order to reduce the liquidity risk during the recession. In the subsets, however, the number of bank relationships for large firms is stable regardless of the business cycle. Unlisted firms, non-chaebol, and low credit quality firms which have relatively limited access to alternative sources of financing show counter-cyclical behavior. Finally, such phenomena is not observed in the non-competitive credit market, while they show a counter-cyclical behavior in the competitive credit market.

• Journal of Distribution Science
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• v.9 no.3
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• pp.25-36
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• 2011

Owing to the rapid growth of hypermarket/discount store formats since 1996, Korean retailing has suddenly attracted the significant attention from researchers. Before the emergence of large scale retailers such as E-Mart, Lotte Mart and Tesco Korea, there were the two retail formats who led the Korean retailing in the modern retailing history: department store and supermarket formats. Nevertheless, there has been little literature concerned about the two retail formats as a case study, while some authors have paid their attention to hypermarket/discount store formats. In addition, when mentioning the development process of retailing history, it is less likely that authors have made an effort to illustrate supermarket retailing history. In order to regard supermarket retailing as part of the Korean retailing, it is interesting to look at a representative supermarket retailer, Haitai, who was one of the subsidiaries of Haitai chaebol. Based on supermarket retailing, the company which was established as a joint venture in 1974 led a supermarket retailing in the Korean modernised retailing history. Before analysing whether Haitai failed or not, the definition of failure should be illustrated. With regard to the term, failure, in the academic world, authors have interchangeably used the following terms: failure, divestment, closure, organisational restructuring, and exit. To collect research data as a case study, the author adopted an in-depth interview method. The research is based on research interviews with 13 ex-staff who left after Haitai went bankruptcy, from store management department to merchandise department. By investigating Haitai's experiences through field interviews, the research found that Haitai restructured organisational decision-making process at the early stage when companies started to modernise organisational charts, benchmarking sophisticated retailing knowledge through the strategic alliance with a Japanese retailer. In respect of buying system, the company established firmly buying functions by adopting central buying system, and further, outstandingly allocated considerable marketing resources to the development of retailer brands with the dedicated team of retailer brand development. In the grocery retailing, abandoning a 'no-frill' packaging concept, the introduction of retailer brand packaging equal to, or better than national brand packaging design, encouraged other retailers to change their retailer brand development strategies. In product sourcing ways, Haitai organised for the first time the overseas sourcing team with the aim of improving the profit margins of foreign products and providing exotic products for customers, followed by other retailers. Regarding distribution system, the company introduced the innovative idea which delivered products ordered by stores directly to each store withboth its own vehicles and its own warehouse in which could deal with dry foods, chilly foods, frozen food, and non-foods, and even, process produce. In addition, Haitai developed many promotional methods to attract more customers like 'the guarantee of the lowest price', and expanded its own business to US in 1996, although withdrew, because of bankruptcy in 1997. Together with POS introduction in 1994, Haitai made a significant contribution to the development of the Korean retailing, influencing other retailers in many aspects. As a case study, the study has provided a number of lessons from Haitai's experiences for academicians and practitioners, suggesting that its history should be involved in the Korean modernised retailing.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.