• Sleep Medicine and Psychophysiology
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• v.3 no.1
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• pp.68-78
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• 1996

Objective : The Mini-Mental State Examination(MMSE) and Brief Cognitive Rating Scale(BCRS) are frequently using screening tests fur evaluating the cognitive function in clinical practice and research. The authors tried to evaluate the clinical usefulness of these tests for the patients with non-traumatic subcortical cerebrovascular disease. Method : We administered the MMSE and BCRS to 85 patients and 195 normal control group. In order to compare the test results according to the lesion site, we divided patients into left sided lesion group(21 patients), right sided lesion group(31 patients) and both sided lesion group(13 patients). Their cognitive function was evaluated by the BNA and daily living functional activity was examined by the IADLs(Instrumental Activities of Daily Living Scale)and GERRI(Geriatric Evaluation by Relative's Rating Instrument). Results : The results are as follows : 1) In the BNA, the patients scored significantly lower than control group at all items(except Right-Left Orientation and Motor Impersistence), but there were no difference in the MMSE(total score and all 5 items), and only 2 items(recent memory and self-care) were significantly different between two groups in the BCRS. 2) In the comparison by lateralization, there were significant differences among three groups at 3 items(Left Tactile Form Perception, Left Finger Localization and Right Finger Localization) in the BNA. But, there were no difference in the MMSE and BCRS. 3) In the correlation between daily living functioning and the MMSE/BCRS, control group showed no relation(except item of cognitive functioning), but patient group was significantly correlated with 3 items(social functioning, instrumental activities of daily living and cognitive functioning). Conclusions : These findings suggest that MMSE and BCRS are not useful as the test for cognitive function and discrimination of lateralization in patients with non-traumatic subcortical cerebrovascular disease. However, scores of these tests may be related with the functional level(such as daily living function) of patients.

• 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.