• Sleep Medicine and Psychophysiology
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• v.22 no.2
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• pp.57-63
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• 2015

Background and Objectives: Internet addiction is an increasing problem in Korea. The previous studies in this area have targeted adolescents and young adults. This study was conducted to examine the risk of internet addiction in Korean adults and the effect of internet addiction on circadian rhythm. Materials and Methods: For this study, 508 subjects were chosen through population proportional sampling to represent the adult population in Korea, 325 of whom were included based on the Alcohol Use Disorder Identification Test-Korea (Audit-K), Zung's Self-Rating Depression Scale (SDS), drug use in the past year, and suicide attempts. In these subjects, sociodemographic factors including age, gender, and residential area were analyzed, and Young's Internet Addiction Scale (IAS), Morningness-Eveningness Questionnaire (MEQ), and an online survey examining sleep onset time on weekdays and weekends, wake-up time, and caffeinated drink intake were executed. Results: Of the 325 subjects, 136 (41.8%) belonged to a high-risk internet addiction group ($IAS{\geq}40$), and 189 (58.2%) belonged to a normal group (IAS < 40). There was a high proportion of male subjects (p = 0.03) in the high-risk group compared to the normal group. There was a high proportion of younger subjects (p = 0.055) in the high-risk group compared to the normal group, but this difference was not statistically significant. Compared to the normal group, there was a high proportion of the evening type ($MEQ{\leq}41$) in the high-risk group (p = 0.024), who also showed a high proportion of caffeinated drink intake (p < 0.001). Also, the high-risk group was found to go to bed and wake up late, but there was no statistically significant difference with the normal group. Conclusion: This study showed that many adults have a high-risk of internet addiction, and there was a significant correlation between internet addiction and sleep in adult, as has been found in adolescents and early adults. In the future, a longitudinal study will be needed to verify the causal relationship between internet addiction and morningness-eveningness.

• International Area Studies Review
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• v.22 no.2
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• pp.163-186
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• 2018

The purpose of this study is to analyze the institutional conditions and problems for the transition to the North Korean economic system. As a research method, we first analyzed the legislative processes of 4th stage market reform policies (liberalization, privatization, privatization, and corporation) by major economic transition countries. And we found out the difference with North Korea. Based on this, it analyzed the process of institutionalization of North Korea's 4th stage economic reform policies (7.1 measures, comprehensive market policies, Currency reform, 6.28 policy). According to research, There are three important conditions that can not compare the changes of the North Korean market economy with those of the transition economies. First, the internal and external conditions and environment for the transition of the economic system and the role of the state and civil society are very different. Second, the means and objectives of the policy decision process and the implementation process are different. Third, it differs absolutely in terms of the nature and effectiveness of the nation's political and economic policies. Fourth, the priority, contents, and legislation process of economic policies for economic reform differ considerably from those of North Korea. Especially, when discussing the possibility of transition to the 'Chinese model', it is accompanied a considerable risk. It is because the purpose of market entry of control power in North Korea and their survival network are quite unique. In addition, China's domestic market size, population size, and type of control are quite different from North Korea. A necessary and sufficient condition for the transition of the North Korean economic system is the relaxation of physical control mechanisms and institutions in the market area. Next, it is necessary to make a legitimate institutionalization as well as an entire survey on the illegal ownership market. Based on this, it is necessary to gradually change the dependence of the domestic market on China to South Korea. In other words, this is a paradigm shift in the semi-controlled power exclusion, post-automation and domestic market.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.