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

• 한국노년학
• /
• v.30 no.3
• /
• pp.973-992
• /
• 2010

The hearing impairment is the representative disorder that affects the quality of the routine life of the aged period. This study was aimed to develop the Korean evaluation scale for hearing handicap(KESHH) with which we can evaluate social and psychological effects of the hearing impairment. Applying this scale clinically, we can analyze the geriatric hearing loss specifically and improve the quality of the aural rehabilitation that can help the hardness of the hearing impairment. Data were collected from 288 participants(176 hearing aid users and 112 non-hearing aid users) and the average age of the participants was 67.4 years old ( 60.15 for the hearing aids users and 78.9 for the non hearing users). The composition ratio of the male and female participants were 58.0% and 42.0% and extrovert and introvert personality were 49.3% and 50.7% showing balanced formation. The tentative draft of KESHH measurements were produced with 30 items and following 5 subscales. Using factor analysis, 6 items were erased and 4 subscales - social effect, psycho/emotional effect, interpersonal effect, and perception of hearing aids - were identified. As each subscale consisted of 6 items, 24 items were corrected and remained totally. Conclusively, the KESHH was developed with 24 items and 4 subscales including 6 items on each subscale. In addition, the KESHH was divided into type-1 and 2 depending on hearing aid users and non hearing aid users. The results of this study can be summarized as the following 5 parts. Firstly, the reliabilities of the KESHH were proved to be high because the subscales' Cronbach alpha values were from 0.723 through 0.895. Secondly, the KESHH showed systematically increasing score as the hearing impairment increased. The lowest score was 24 and the highest score was 117 and the average scores of the hearing impaired and non-hearing impaired are 72.06(SD=15.67) and 66.98(SD=20.94) showing 5.08 increased score for the hearing impaired. Depending on the degree of the hearing loss, the scores recorded 52.63 at the below of the mild hearing loss, 67.29 for the moderate hearing loss, 71.89 for the moderately severe hearing loss, and 75.57 for the severe hearing loss The comparison of the scores by hearing levels indicated that the higher the hearing levels were, the higher the scores of the KESHH with statistical significance(p<0.001). Thirdly, the correlation among 4 subscales was 0.384~0.880(p<0.001). Also, the pure tone average, personality, and the four subscales correlations showed statistical significance with 0.148~0.880 except for the pure tone average and personality and the pure tone average and perception of hearing aids. Fourthly, the total variances explained for the independent subscles were analyzed with multiple regression. The social effect was explained 17.4% with pure tone average, personality, and the status of hearing aid use variances. The psycho/emotional effect was explained 14.4% with puretone average, personality, and age variances. The interpersonal effect was explained 11.2% with pure tone average, personality, and the status of hearing aid use variances. The perception of hearing aids effect was explained 2.2% with only personality. Finally, test-retest reliability was proved to be high with 0.791(p<0.001). Conclusively, the KESHH that was developed considering Korean culture can be a useful instrument for expressing the hearing handicaps of the Korean aged hearing impaired in scores for both hearing aid users and non-users. Also, it is thought that the KESHH is useful clinically for identifying the changes of the hearing handicap scores before and after wearing hearing aids and aural rehabilitation at diverse situations.