• Proceedings of the KSR Conference
• /
• 2011.10a
• /
• pp.2473-2478
• /
• 2011

The asset management of railway facilities is a total framework for finally supporting a safe and comfortable train service, which includes functions of supporting evaluation of condition and performance of infrastructures, making the decision method of repair or rehabilitation of deteriorated facilities, and lengthening the life cycle of structure through the decision of adequate cost and time of repair or reinforcement. In the range of the asset management, organization, human, the target, and information & data of company are included. Therefore, in this paper, appling the method of asset management analysis to the railway structures, the process of the risk assesment using BRE(Business Risk Exposure) and the basis of consisting optimized renewal decision-making are expressed.

• Journal of Korean Society for Quality Management
• /
• v.31 no.3
• /
• pp.160-171
• /
• 2003

Criteria for the comparison of quality of asset portfolio management are risk and return. In this paper a method to use structural time-series model to determine an optimal portfolio for the improvement of quality of asset portfolio management is suggested. In traditional mean variance analysis expected return is assumed to be time-invariant. However, it is more realistic to assume that expected return is temporally dynamic and structural time-series model can be used to reflect time-varying nature of return. A data set from an insurance company was used to show validity of suggested method.

• Journal of the Korean Home Economics Association
• /
• v.36 no.7
• /
• pp.57-67
• /
• 1998

This study was to compare the financial status between elderly households - retired vs employed. The sample obtained from 1994 KHPS, and consisted of 628 Korean aged households who are currently married. Statistics employed to analyze the data are mean, frequency, percentile, t-test, and relative-ratio. The results of this study were as follows ; In income sources, earned income was majority of employed households, but the percent of unearned income was greater than retired households. While the percent of cloth, education, recreation expenditures were high in employed, and medical, housing expenditures wee high percentage in retired. The percentage of real asset(housing) was majority of total asset in two groups. And the percentage of safe liquid asset of retired households was relatively higher than employed households.

• The Pure and Applied Mathematics
• /
• v.28 no.4
• /
• pp.329-341
• /
• 2021

In this study, we consider an efficient and accurate finite difference method for the four underlying asset equity-linked securities (ELS). The numerical method is based on the operator splitting method with non-uniform grids for the underlying assets. Even though the numerical scheme is implicit, we solve the system of discrete equations in explicit manner using the Thomas algorithm for the tri-diagonal matrix resulting from the system of discrete equations. Therefore, we can use a relatively large time step and the computation of the ELS option pricing is fast. We perform characteristic computational test. The numerical test confirm the usefulness of the proposed method for pricing the four underlying asset equity-linked securities.

• Journal of the Korean Society of Industry Convergence
• /
• v.25 no.4_2
• /
• pp.645-653
• /
• 2022

Recently, deep reinforcement learning has been applied to a variety of industries, such as games, robotics, autonomous vehicles, and data cooling systems. An algorithm called reinforcement learning allows for automated asset allocation without the requirement for ongoing monitoring. It is free to choose its own policies. The purpose of this paper is to carry out an empirical analysis of the performance of asset allocation strategies. Among the strategies considered were the conventional Mean- Variance Optimization (MVO) and the Proximal Policy Optimization (PPO). According to the findings, the PPO outperformed both its benchmark index and the MVO. This paper demonstrates how dynamic asset allocation can benefit from the development of a reinforcement learning algorithm.

• Asia Marketing Journal
• /
• v.16 no.1
• /
• pp.169-193
• /
• 2014

The earlier studies have verified that brand values have significant impact on financial values such as stock return and stock price to justify marketing costs for brand building. Except for Mizik and Jacobson (2008), however, little research has addressed what kinds of brand components composing brand values have a significant relationship with financial values. As a follow-up research of Mizik and Jacobson (2008), this research focuses on what kinds of relationships exist between the unanticipated change of each brand asset component and stock return, one of the financial values. The authors selected six brand asset components from the Korea-Brand Power Index(K-BPI) data in which 'Top of Mind,' 'Unaided Awareness,' and 'Aided Awareness' are brand awareness measures and 'Image,' 'Purchase Intention,' and 'Preference' are brand loyalty measures. Out of those six brand components, they found that unanticipated changes of 'Top of Mind,' 'Unaided Awareness,' 'Image,' and 'Preference' have significantly positive effect on unexpected stock return change. Therefore, they conclude that these four brand asset components provide incremental information in explaining unanticipated stock return.

• Journal of the Korean Mathematical Society
• /
• v.38 no.5
• /
• pp.1047-1059
• /
• 2001

The classical Geometric Brownian motion (GBM) model for the price of a risky asset, from which the huge financial derivatives industry has developed, stipulates that the log returns are iid Gaussian. however, typical log returns data show a distribution with much higher peaks and heavier tails than the Gaussian as well as evidence of strong and persistent dependence. In this paper we describe a simple replacement for GBM, a fractal activity time Geometric Brownian motion (FATGBM) model based on fractal activity time which readily explains these observed features in the data. Consequences of the model are explained, and examples are given to illustrate how the self-similar scaling properties of the activity time check out in practice.

• The Journal of the Korea Contents Association
• /
• v.10 no.11
• /
• pp.68-79
• /
• 2010

With the social infrastructure being deteriorated, there is a growing need to introduce the asset management to social infrastructure management in order to increase their value and save budget. Social infrastructure asset management is a new concept of facility management in response to these demands. It is defined as a procedure for collecting and analysing facility maintenance data and for making and practicing an economically optimized management plan. Detailed survey work of asset management business is analyzed in order to derive a strategy for asset management information. The contents of IIMM of New Zealand and the asset management definition of the FHWA of the United States, and representative facility management systems of Korea are analysed. The role between organizations and the relationship between business and organization were analyzed. Information required for asset management and for existing facility management systems is compared with business of asset management. In this thesis, three development strategies are suggested. The first one is to develop core business of asset management while excluding duplicated development. The second one is to divide system's structure into three layers. And the last one is to share information by interfacing asset management systems with existing facility management systems.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.135-149
• /
• 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 Korean Home Economics Association
• /
• v.41 no.9
• /
• pp.105-120
• /
• 2003

This study has conducted to develop the computer program for households portfolio management to enhance their financial well-being. The study has divided into two parts. First, descriptive statistics has used to analyze as a basis of computer program and it includes the comparison of household asset allocations between households in Korea and U. S. A., Second, it shows the components of the portfolio program developed to manage households efficiently. For both two countries, recent four years data has been used and in part two, total sample size of households in Korea is 2155. From the statistical analysis, households in U. S. A. tend to invest more on the stock & bonds as their net-asset is increased. However households in Korea tend to have less financial assets and it might be found the fact that they prefer to own real-estate because of the inflation. In the part of computer program, it is included the average financial asset responding to the demographic variables and households could refer those average amount as a reference planning their asset portfolio.