• 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 the Society of Cosmetic Scientists of Korea
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• v.23 no.1
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• pp.5-20
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• 1997

War and poverty depress the consumption of cosmetics, while peace and prosperity encourage their proliferation. With the end of World War II, the US, Europe and Japan witnessed rapid growth of their cosmetic industries. The ending of the Cold War has stimulated the growth of the industry in Eastern Europe. Improved economies, and mass communication are also responsible for the fast growth of the cosmetic industries in many Asian nations. The rapid development of the cosmetic industry in mainland China over the past decade proves that changing economies and political climates can deeply affect the health of our business. In addition to war, economy, political climate and mass communication, factors such as lifestyle, religion, morality and value concepts, can also affect the growth of our industry. Cosmetics are the product of the society. As society and the needs of its people change, cosmetics also evolve with respect to their contents, packaging, distribution, marketing concepts, and emphasis. In many ways, cosmetics mirror our society, reflecting social changes. Until the early 70's, cosmetics in the US were primarily developed for white women. The civil rights movement of the 60's gave birth to ethnic cosmetics, and products designed for African-Americans became popular in the 70's and 80's. The consumerism of the 70's led the FDA to tighten cosmetic regulations, forcing manufacturers to disclose ingredients on their labels. The result was the spread of safety-oriented, "hypoallergenic" cosmetics and more selective use of ingredients. The new ingredient labeling law in Europe is also likely to affect the manner in which development chemists choose ingredients for new products. Environmental pollution, too, can affect cosmetics trends. For example, the concern over ozone depletion in the stratosphere has promoted the consumption of suncare products. Similarly, the popularity of natural cosmetic ingredients, the search of non-animal testing methods, and ecology-conscious cosmetic packaging seen in recent years all reflect the profound influences of our changing world. In the 1980's, a class of efficacy-oriented skin-care products, which the New York Times dubbed "serious" cosmetics, emerged in the US. "Cosmeceuticals" refer to hybrids of cosmetics and pharmaceuticals which have gained importance in the US in the 90's and are quickly spreading world-wide. In spite of regulatory problems, consumer demand and new technologies continue to encourage their development. New classes of cosmeceuticals are emerging to meet the demands of increasingly affluent Asian consumers as we enter the 21st century. as we enter the 21st century.