• The Journal of Industrial Distribution & Business
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• v.12 no.2
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• pp.41-52
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• 2021

Purpose: This study aims to provide implications for the government's housing supply policy by analyzing the factors that determine the type of real estate holding and household debt. This study started from the awareness that the determinants of household debt differ depending on the type of real estate holding. Research design, data and methodology: Real estate ownership type was classified and analyzed into 4 models: model 1 (1 household 1 house and self-resident), model 2 (1 household multiple real estate ownership and self-resident), model 3 (1 household 1 house and rent residence), model 4 (1 household holds a large number of real estate and rent residence). The analysis method used multiple regression analysis. The dependent variable was household total debt. As independent variables, household debt, annual gross household income, financial assets, real estate net assets, annual repayment, demographic & residential characteristics were used. Results: 1) Model 4 has the highest household debt and the highest gross income, Model 2 has the most real estate mortgage loans and real estate net asset, and Model 1 has the highest real estate mortgage payments. 2) The positive factor of common household debt determinants is real estate net assets, and the negative factor is financial assets. 3) It was the net assets of real estate that acted as a positive factor in common for the four models. In other words, the more financial assets, the less household debt. It was analyzed that the more net assets of real estate, the more household debt. The annual repayment of financial liabilities had no influence on household debt, while the annual repayment of loan liabilities and household debt had a positive relationship. Conclusions: 1) It is necessary to introduce benefits and systems that can increase the proportion of household financial asset. Specific alternatives include tax benefits and reduced fees for financial asset investment. 2) In the case where a homeless person prepares one house for one household, it is necessary to prepare various support measures according to the income level. The specific alternative is to give additional points for pre-sale or apply an interest rate cut incentive for mortgage loans.

• Journal of Service Research and Studies
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• v.6 no.2
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• pp.99-116
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• 2016

The purpose of this paper is to see what the problem is and what the direction of the strategy of asset management after this study has analyzed asset management status of domestic life insurance companies according to interest rate trends, analyzing in time series management asset lists, asset distribution state, and securities list of life insurance companies during 2000~2014. It has carried correlation analysis and regression analysis between yield and bond interest KOSPI index. As the study result, life insurance companies have managed assets in stability than profitability. The correlation coefficient between interest rate and performance rates of total asset, management asset and securities is highly plus, correlation of management asset performance rate is higher than that of total asset performance rate, and the correlation of securities performance rate is higher than that of management asset performance rate. The correlation coefficient of KOSPI and performance rate shows minus. The suggestion is that the change of asset management is required as the interest decline rises up a reverse margin risk because of the asset management of stability.

• Journal of the Korean Society of Safety
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• v.30 no.3
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• pp.67-72
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• 2015

Although attention to terrorism has increased sharply in recent years within many countries, it is by no means a new phenomenon. Majority of these countries have limited regulations or guidelines about terrorism. LOP (Level Of Protection) can be consider as a first step. This paper seeks to present a process to determine LOP and allowable damage. LOP is determined by asset value reason why it should be based on cost. The asset value is defined as "cost induced when asset is damaged". For example, the collateral damage outside the facility should be taken in consideration in the asset value. Allowable structural damage is assigned depending on LOP.

• Journal of Internet Computing and Services
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• v.9 no.4
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• pp.79-84
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• 2008

In these days, many organizations try to manage their assets in safe way due to fast change in information-communication environment. In Korea, risk analysis and vulnerability analysis for security improvement of critical asset is booming by enforcement of Act on security of information and communication infrastructure. It is obligate that each critical information infrastructure needs to get vulnerability analysis. In this paper, we proposed Object Oriented Asset Classification model for asset analysis and risk analysis.

• Korean Journal of Construction Engineering and Management
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• v.11 no.6
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• pp.35-44
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• 2010

For efficient maintenance management of bridges, an establishment of asset management system is necessary which helps prediction of maintenance cost and strategic allocation of budget in consideration of top priority. The main purpose of this study is to suggest asset valuation method, which is practical in conformity with domestic situations, through researches on asset valuation method of bridges. This study has researched asset valuation method of bridge, which is appropriate for domestic situations by finding out advantages and disadvantages through investigating domestic and foreign application examples of asset valuation method for bridge facilities. In this study, asset valuation method by historical cost and replacement cost were suggested and a valuation model for bridges was established. In addition, two suggested valuation methods were applied to actual bridges which is used in Korea. As the result, it was analyzed that bridge asset valuation method in consideration of historical cost is desirable for the accounting purpose. And, it was analyzed that valuation method utilizing depreciated replacement cost(DRC), which could consider various factors, is desirable for the maintenance decision supporting purpose.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.426-428
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• 2004

Sensor network supports data delivery from physical world to cyber space Sensors get physical events then wireless network transfers sensor data to service server. We use sensor network technology to manage location information of asset. In ubiquitous computing environment, user localization is basic context for intelligent service. A lot of research group make effort to develop low cost localization technology. In this paper, we propose asset monitoring system using wireless sensor network. It is implemented using ad hoc network technology which can be adopted to smart home and this system can monitor the asset location and movement.

• East Asian Economic Review
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• v.22 no.3
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• pp.371-400
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• 2018

Although asset price is an important factor in determining changes in external balances, no studies have investigated it from the Chinese perspective. In this study, I empirically examine the underlying driving forces of China's trade balances, particularly the role of asset price and the real exchange rate. To this end, I estimate a sign-restricted structural vector autoregressive model with quarterly time series data for China, using the Bayesian method. The results show that changes in asset price affect China's trade balances through private consumption and investment. Also, an appreciation of the real exchange rate tends to deteriorate trade balances in China. Furthermore, forecast error variance decomposition results indicate that changes in asset price (stock price and housing price) explain about 20% variability of trade balances, while changes in the real exchange rate can explain about 10%.

• 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 Convergence Society for SMB
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• v.6 no.4
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• pp.151-156
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• 2016

It has been constantly demanding for effective way to improve software development productivity and automation. In this paper, we did research to configure the component assets to reuse design information from the design phase domains based on the DMI. It is necessary abstracted information architecture of an independent platform development environment to reuse design information on the design stage. Also, It should be based on a well-designed to support the design architecture of the application domain. Therefore, in this paper, I want to use the DMI architecture that can be represented by formal level of architectural design information platform and application domain area. It is able to decomposition architecture asset with the part design information from the design phase composition or a high level of abstraction on DMI. Therefore, the metadata structure of the asset architecture will support a structure which can reuse the structure-based design of the domain areas.

• Journal of Digital Convergence
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• v.7 no.2
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• pp.63-70
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• 2009

It is well known that in the knowledge-based economy a firm's intellectual property including patent is crucial for its competitive advantage. In industrial economy, tangible assets such as land and equipment have been of critical importance for a firm's competitiveness. However, as the paradigm shift to the knowledge-base economy is being accelerated, intangible assets such as intellectual property are considered to be more influential for a firm to be competitive. The valuation of intangible asset is as necessary as that of tangible asset. In particular, reasonably accurate valuation of patent which represents a firm's technological competitiveness seems to be of great importance since (1) it is essential for a firm's patent strategy development, (2) it can activate the technology market, and (3) it is necessary to evaluate a patent's technological contribution when multiple patents are used to develop and commercialize an innovative product. Unlike that of tangible asset, the valuation of intangible asset is very difficult. In order to figure out a value of patent it is necessary to use a number of appropriate valuation factors. In this paper, we surveyed various valuation factors presented by previous researches and several technology valuation organizations to find a set of common valuation factors considered to be of more importance.