• 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.

• Cartoon and Animation Studies
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• s.33
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• pp.31-53
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• 2013

In the early twentieth century, history of animation began by modern artists, they produced various experimental images with the newly invented film and cameras. Artists in the field of movie, photography, paintings and others manipulated images in motion. But as some animated movies won industrial success and popularity, they became the trend but experimental style of early animation preserved by so-called non-mainstreamers or experimental animators, counteracting commercialism. Disney animation also followed the trend by applying realistic Hollywood film style, the worse critics placed a low value on the animation and it tarnished the image, although it was profitable investment from a business standpoint. To make images realistic, they opened a drawing class that animators developed skills to imitate motions and forms from subjects in real life. Also some techniques and gizmos were used to mimic and simulate three dimensional objects and spaces, multiplane camera and compositing 3D CG images with 2D drawings. Moreover, they brought animation stories from fairly tales or folk tales, and Walt's personal interest in live-action movies, they applied Hollywood-film-like narratives and realistic visual, and harsh criticism ensued. On the surface early disney animations' potential seems to be weakened, but in reality it still exists by simplifying and exaggerating forms and color as modern arts. Disney animation employs concepts of the modernism paintings such as simplified shapes and colors to a character design, when their characters are placed together in a scene, that visual elements cause mental reaction. This modification gives a new internal experience to audiences. As conceptual colors in abstract paintings make images appeared to be flat, coloring characters with no shading make them look flat and comparing to them, background images are also appeared to be flat. On top of that, multi-perspective at background images recalls modernist paintings. This essay goes in details with the animation pioneers' works and how Disney animation developed its techniques to emulate real life and analyses color schemes, forms, and spaces in Disney animation compared with modern artists' works, in that the visual language of Disney animation reminds of impression from abstract paintings in the beginning of the twentieth centuries.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.4
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• pp.233-244
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• 2018

The purpose of this study was to improve awareness of network marketing business in South Korea, which takes a quite negative view of network marketing, to raise awareness of "starting up a business" as independent business owners, to assist them in building their own subjecthood in network business and entrepreneurial identity, and ultimately to give some suggestions on how to improve their self-directed entrepreneurial competency and the quality of their business management. To make an empirical analysis of 121 independent business owners of Korean native network marketing, the personal and background variables of the entrepreneurs were selected as independent variables, and entrepreneurship was selected as a mediator variable. The selected dependent variables were financial and non-financial business performances. A multiple regression analysis was conducted by inputting the variables. The findings of the study were as follows: It produced an effect of more financial performance when the innovation of the independent business owners of network marketing was better, and this innovation was possible to have only when they improved in entrepreneurial efficacy and locus of control. In contrast, only authenticity had an effect on boosting non-financial business performance among the factors of entrepreneurship. To have authenticity, it's necessary to strengthen achievement needs and awareness of locus of control among the personal characteristics of the independent business owners of network marketing. Among the factors of entrepreneurship, there was better authenticity when their personal networks were broader among their background characteristics. In addition, self-efficacy that was one of personal characteristics made a direct contribution to the enhancement of non-financial performance. As a result, independent business owners of network marketing are required to hold a strong belief in their own business, to be active in business activities and to have a high efficacy as spontaneous principal agents, and they also are required to have more adventurous, planned, active and propulsive achievement needs that enable them to attain the goals of their business and keep moving forward. Besides, they should foster their locus-of-control competency that is to control, endure and be responsible for a variety of phenomena or incidents that they face, and there will be better financial performance or non-financial performance only when they demonstrate their self-belief and confidence and when they have faith in and conviction about themselves. For independent business owners of network marketing, a consumer-centered thinking that entails authenticity and trustworthiness and touches the hearts of customers is a more effective marketing strategy than an egocentric thinking as sellers when they approach customers. And they are expected to make progress in terms of the quality of business management when their business attachment or enthusiasm is great enough to match their own efficacy with entrepreneurial identity or strike a balance between them.

• The Korean Journal of Financial Management
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• v.21 no.2
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• pp.125-151
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• 2004

This study tests the price discovery from US Treasury bond markets to Korean bond markets using the daily returns of Korean bond data (CD, 3-year T-note, 5-year T-note, 5-year corporate note) and US treasury bond markets (3-month T-bill, 5-year T-note 10-year T-bond) from July 1, 1998 to December 31, 2003. For further research, we divide full data into two sub-samples on the basis of the start-up of bond valuation system in Korean bond market July 1, 2000, employing uni-variate AR(1)-GARCH(1,1)-M model. The main results are as follows. First the volatility spillover effects from US Treasury bond markets (3-month T-bill, 5-year T-note, 10-year T-bond) to Korean Treasury and Corporate bond markets (CD, 3-year T-note, 5-year T-note, 5-year corporate note) are significantly found at 1% confidence level. Second, the price discovery function from US bond markets to Korean bond markets in the sub-data of the pre-bond valuation system exists much stronger and more persistent than those of the post-bond valuation system. In particular, the role of 10-year T-bond compared with 3-month T-bill and 5-year T-note is outstanding. We imply these findings result from the international capital market integration which is accelerated by the broad opening of Korean capital market after 1997 Korean currency crisis and the development of telecommunication skill. In addition, these results are meaningful for bond investors who are in charge of capital asset pricing valuation, risk management, and international portfolio management.