• 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.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.3
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• pp.257-264
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• 2006

This study investigated the effects of hybrid process(chemical coagulation, Fenton oxidation and ceramic UF(ultrafiltration)) on COD and color removals of commercial reactive dyestuffs. In the case of chemical coagulation, the optimal concentrations of $Fe^{3+}$ coagulant for COD and color removals of RB49(reactive blue 49) and RY84(reactive yellow 84) were determined according to the different coagulant dose at the optimal pH. They were 2.78 mM(pH 7) in RB49 and 1.85 mM(pH 6) in RY84, respectively. In the case of Fenton oxidation, the optimal concentrations of $Fe^{3+}\;and\;H_2O_2$ were obtained. Optimal $[Fe^{2+}]:[H_2O_2]$ molar ratio of COD and color removals of RB49 and RY84 were 4.41:5.73 mM and 1.15:0.81 mM, respectively. In the case of ceramic UF, the flux and rejection of supernatant after Fenton oxidation were investigated. After ceramic UF for 9 hr, the average flux of RB49 and RY84 solutions were $53.4L/m^2hr\;and\;67.4L/m^2hr$ at 1 bar, respectively. In addition, the permeate flux increased and the average flux recovery were 98.5-99.9%(RB49) and 91.0-97.3%(RY84) according to adopting off-line cleaning(5% $H_2SO_4$). Finally, COD and color removals were 91.6-95.7% and 99.8% by hybrid process, respectively.