• Cartoon and Animation Studies
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• 통권47호
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• pp.81-101
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• 2017

The present study argues that film director Lee Ae-rim animation works depict the world of the grotesque and not only are important sociocultural phenomena but also hold the significance of humor and subversion. The grotesque exhibits the intriguing characteristics of expressing a perspective critical of the existing society through the sensibilities of minorities and is used broadly as a term not only in the aesthetic sense but also designating sociocultural phenomena. Although discussed separately in terms of Mikhail Bakhtin's carnival grotesque and Mary Russo's uncanny grotesque, the grotesque fundamentally rejects existing order and conventions and is externalized through unique expressions, thus opening up a rich possibility for rejection, humor, satire, transformation, and deconstruction of and regarding the authority of the mainstream. Although they constitute a fictional medium, animation films are social products as well so that they are affected by society, culture, and history and stand as important texts that must be interpreted in terms of the relationships between humans' instinctive desires and society and between the overall culture and artistic media. However, the rarity of grotesque portrayals in South Korean animation films also proves that it is a society where even problems that are in themselves sensitive must be manifested ingeniously on a conventional level. South Korean society has a unique history of colonialism and national division and is simultaneously in the unique situation of a society that has undergone growth at a nearly unprecedented rate. Consequently, the society exhibits closed yet dynamic particularity where everyday tension and rigidity, wariness of others and extreme competition are intertwined in a complex manner. Intensively analyzed in the present discussions, director Lee's animation films and are characterized mainly by grotesque images, nonlinear narratives, and vivid depictions. In such a context, these works not only are artistic products of South Korean society but also rejections of a rigid society and share the significance of the aesthetics of the carnival grotesque, which consists of subversive expressions directed at a new world.

• Journal of Intelligence and Information Systems
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• 제26권2호
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.