• 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.

• Asia Marketing Journal
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• v.12 no.2
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• pp.155-175
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• 2010

Most of the service industries including finance and telecommunications have become matured and saturated. The competitions have become severe while the differences among brands become smaller. Therefore maintaining good relationships with customers has been critical for the service providers. In case of credit card and debit card, the similar patterns are shown. It is important for them to maintain good relationships with customers, and therefore, they have used marketing program which provides customized services to customers and utilizes the membership programs. Not only do they build and maintain good relationships, but also highlight their brands from the emotional aspects. For example, KB Card or Hyundai Card uses well-known designers' works for their credit card design. As well, they differentiate the designs of credit cards to stress on their brand personalities. BC Card introduced the credit card with perfume that a customer would like. Even though the credit card is small and not shown to public easily, it becomes more important for those companies to touch the customers' feelings with the brand personalities and their images. This is partly because of changes in consumers' lifestyles. Y-generations becomes highly likely to express themselves in many different ways and more emotional than X-generations. For the Y-generations, therefore, even credit cards in the wallet should be personalized and well-designed. In line with it, credit cards with good design can be seen as an example of brand identity, where different design for each customer can be used to recognize the membership groups that customers want to belong. On the other hand, these credit card companies offer the special treatment benefits for those customers who are heavy users for the cards. For example, those customers who love sports will receive some special discounts when they use their credit cards for sports related products. Therefore this study attempted to explore the relationships between relational benefits, brand identification and loyalty. It has been well known that relational benefits and brand identification lead to loyalty independently from many other studies, but there has been few study to review all the three variables all together in a research model. Furthermore, as reviewed above, in the card industry, many companies attempt to associate the brand image with their products to fit their customers' lifestyles while relational benefits are still playing an important role for their business. Therefore in our research model, relational benefits, brand identification, and loyalty are all included. We focus on the mediating effect of brand identification. From the relational benefits perspective, only special treatment benefit and confidence benefit are included. Social benefit is not applicable for this credit card industry because not many cases of face-to-face interaction can be found. From the brand identification perspective, personal brand identity and social brand identity are reviewed and included in the model. Overall, the research model emphasizes that the relationships between relational benefits and loyalty will be mediated by the effect of brand identification. The effects of relational benefits which are confidence benefit and special treatment benefits on loyalty will be realized when they fit to the personal brand identity and social brand identity. In the research model, therefore, the relationships between confidence benefit and social brand identity, and between confidence benefit and personal identity are hypothesized while the effects of special treatment benefit on social brand identity and personal brand identity are hypothesized. Loyalty, then, is hypothesized to have positive relationships with personal brand identity and social brand identity. In addition, confidence benefit among the relational benefits is expected to have a direct, positive relationship with loyalty because confidence benefit has been recognized as a critical factor for good relationships and satisfaction. Data were collected from college students who have been using either credit cards or debit cards. College students were regarded good subjects because they are in Y-generation cohorts and have tendency to express themselves more. Total sample size was two hundred three at the beginning, but after deleting those data with many missing values, one hundred ninety-seven data points were remained and used for the model testing. Measurement items were brought from the previous literatures and modified for this research. To test the reliability, using SPSS 14, chronbach's α was examined and all the values were from .874 to .928 exceeding over .7. Using AMOS 7.0, confirmatory factor analysis was conducted to investigate the measurement model. The measurement model was found good fit with χ²(67)=188.388 (p= .000), GFI=.886, AGFI=.821, CFI=.941, RMSEA=.096. Using AMOS 7.0, structural equation modeling has been used to analyze the research model. Overall, the research model fit were χ²(68)=188.670 (p= .000), GFI=.886, AGFI=,824 CFI=.942, RMSEA=.095 indicating good fit. In details, all the paths hypothesized in the research model were found significant except for the path from social brand identity to loyalty. Personal brand identity leads to loyalty while both confidence benefit and special treatment benefit have a positive relationships with personal and social identities. As well, confidence benefit has a direct positive effect on loyalty. The results indicates the followings. First, personal brand identity plays an important role for credit/debit card usage. Therefore even for the products which are not shown to public easy, design and emotional aspect can be important to fit the customers' lifestyles. Second, confidence benefit and special treatment benefit have a positive effects on personal brand identity. Therefore it will be needed for marketers to associate the special treatment and trust and confidence benefits with personal image, personality and personal identity. Third, this study found again the importance of confidence and trust. However interestingly enough, social brand identity was not found to be significantly related to loyalty. It can be explained that the main sample of this study consists of college students. Those strategies to facilitate social brand identity are focused on high social status groups while college students have not been established their status yet.