• Journal of Digital Convergence
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• v.17 no.12
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• pp.229-234
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• 2019

The software market has diversified service needs due to the expansion of the mobile market. To this end, the company intends to produce various apps by extending to the design domain based on the structured architectural assets of the domain market. In this study, we propose an evaluation model that can evaluate the efficiency for servicing assets that reflect the domain characteristics of architecture based on cloud. Based on the characteristics of ISO/IEC 25010 quality model of SQuaRE Series, a software evaluation standard, evaluation model sub-features for evaluating the efficiency of cloud-based asset data were constructed. When the architectural assets were designed as composite assets, they were designed to provide the flexibility of the evaluation model by applying the mandatory and optional evaluation elements of the sub-features that weighted the evaluation items according to the characteristics of the design domain.

• Advances in nano research
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• v.15 no.1
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• pp.49-57
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• 2023

As composite materials are used in many applications, the modern world looks forward to significant progress. An overview of the application of composite fiber materials in sports equipment is provided in this article, focusing primarily on the advantages of these materials when applied to sports equipment, as well as an Analysis of the influence of sports equipment of fiber-reinforced composite material on social sports development. The present study investigated surface morphology and physical and mechanical properties of S-glass fiber epoxy composites containing Al₂O₃ nanofillers (for example, 1 wt%, 2 wt%, 3 wt%, 4 wt%). A mechanical stirrer and ultrasonication combined the Al₂O₃ nanofiller with the matrix in varying amounts. A compression molding method was used to produce sheet composites. A first physical observation is well done, which confirms that nanoparticles are deposited on the fiber, and adhesive bonds are formed. Al₂O₃ nanofiller crystalline structure was investigated by X-ray diffraction, and its surface morphology was examined by scanning electron microscope (SEM). In the experimental test, nanofiller content was added at a rate of 1, 2, and 3% by weight, which caused a gradual decrease in void fraction by 2.851, 2.533, and 1.724%, respectively, an increase from 2.7%. The atomic bonding mechanism shows molecular bonding between nanoparticles and fibers. At temperatures between 60 ℃ and 380 ℃, Thermogravimetric Analysis (TGA) analysis shows that NPs deposition improves the thermal properties of the fibers and causes negligible weight reduction (percentage). Thermal stability of the composites was therefore presented up to 380 ℃. The Fourier Transform Infrared Spectrometer (FTIR) spectrum confirms that nanoparticles have been deposited successfully on the fiber.

• Journal of Navigation and Port Research
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• v.43 no.2
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• pp.134-141
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• 2019

The objective of this study was to establish whether global macroeconomic indicators affect the profitability of Korean shipping companies by using panel regression analysis. OROA (operating return on assets) and ROA (ratio of net profit to assets) were selected as proxy variables for profitability. OROA and ROA were used as dependent variables. The world GDP growth rate, interest rate, exchange rate, stock index, bunker price, freight, demand and supply of the world shipping market were set as independent variables. The size of the firm was added to the control variable. For small-sized firms, OROA was not affect by macroeconomic indicators. However, ROA was affected by variables such as interest rates, bunker prices, and size of firms. For medium-sized firms, OROA was affected by demand, supply, GDP, freight, and asset variables. However, macroeconomic indicators did not affect ROA. For large-sized firms, freight, GDP, and stock index (SCI; Shanghai Composite Index) have an effect on OROA. ROA was analyzed to be influenced by bunker price and SCI.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.3
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• pp.393-402
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• 2014

ICT environment to the survey released by the Bureau of Statistics 2012 Household Finance. Korean Welfare survey 24.9% of all households in financial assets, real estate is about three times more than 69.9%, respectively. The problem is that the information is slow and income deciles(deciles 1-4), a relatively high proportion of households with low(78.8 to 69%) of the real estate assets of the expansion of the world economy with low growth and low uncertainty, work from home due to the information changes in the structure of the economy, such as increases in real estate prices remain exposed to the risk of a phenomenon such as Pour House Pour Talent and low-income people is bound to be more serious symptoms. This low correlation is by constructing a composite asset portfolio, the weighted average risk of the individual assets while increasing overall revenue decrease that risk is based on the principle of portfolio by type and different areas in the ICT environment in a portfolio of real estate price index low correlation to financial assets by including the effect of dispersion stable complex asset portfolio and empirical Growth was divided.

• International Area Studies Review
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• v.15 no.3
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• pp.393-405
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• 2011

Three different models have been consecutively employed with the U.S. yield curve and the Korean composite stock price index, firstly to see the coupling between the economies of the U.S. and Korea, secondly to find out the time consumed completing the coupling, and lastly to figure out the impact of the recent U.S. financial crisis on this coupling. This study has, first of all, produced an empirical research outcome which proved the existence of coupling between two countries' economies. The direction of this coupling was consistent with the general expectation that when the yield spread between the U.S. 10-year Treasury Note and the U.S. 3-month Treasury Bill increased which often occurred with better prospects of U.S. economy, the asset price of emerging economies including Korea also rose reflecting the accompanying change in investment atmosphere in favor of risk. It has also found out that the degree of the coupling was maximized with a lag of one week. And finally the recent US financial crisis has been revealed to reduce the degree of the coupling by as much as half in a regression model with a dummy variable.

• Asia-Pacific Journal of Business
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• v.14 no.2
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• pp.51-61
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• 2023

Purpose - Since COVID-19, the government's expansion of liquidity to stimulate the economy has resulted in an increase in private debt and an increase in asset prices of such as real estate and stocks. The recent sharp rise of the US Federal fund rate and tapering by the Fed have led to a fast rise in domestic interest rates, putting a heavy burden on the Korean economy, where the level of household debt is very high. Excessive household debt might have negative effects on the economy, such as shrinking consumption, economic recession, and deepening economic inequality. Therefore, now more than ever, it is necessary to identify the causes of the increase in household debt. Design/methodology/approach - Main methodology is regression analysis. Dependent variable is household loans from depository institutions. Independent variables are consumer price index, unemployment rate, household loan interest rate, housing sales price index, and composite stock price index. The sample periods are from 2017 to May 2022, comprising 72 months of data. The comparative analysis period before and after COVID-19 is from January 2017 to December 2019 for the pre-COVID-19 period, and from Jan 2020 to December 2022 for the post-COVID-19 period. Findings - Looking at the results of the regression analysis for the entire period, it was found that increases in the consumer price index, unemployment rate, and household loan interest rates decrease household loans, while increases in the housing sales price index increase household loans. Research implications or Originality - Household loans of depository institutions are mainly made up of high-credit and high-income borrowers with good repayment ability, so the risk of the financial system is low. As household loans are closely linked to the real estate market, the risk of household loan defaults may increase if real estate prices fall sharply.

• Journal of Distribution Science
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• v.17 no.4
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• pp.59-68
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• 2019

Purpose - The purpose of this study is to examine the differential mediating effects of three dimensions of buyer trust in the influence of supplier characteristics on the relationship performance. In this study, transaction characteristics were classified into competences and assets. The corporate reputation is considered as intangible assets, the customer-linking capability is considered among the competencies and transaction specific asset is selected from tangible assets. This study is also to examine the moderating effect of technical environment uncertainty in the effects of integrity and benevolence on the intention to continue trading. This study aims to provide a guide on which dimension suppliers should manage and how to improve their trust in order to maintain business with companies in technical environment uncertainty. Research design, data, and methodology - The data for the empirical analysis of this study were obtained by interviewing the 274 purchasing managers of Daegu - Gyeongbuk small and medium enterprises. The items used in this survey were partially modified to fit the characteristics of the B2B industry. The reliability and validity of the variables were analyzed using SPSS 18.0 and AMOS 18.0 programs and hypotheses were verified through the structural equation modeling. Results - In this study, reliability was examined by Cronbach 'α test. Composite Reliability and Average Mean Variance extracted value exceeded the baseline values. As a result of hypotheses testing, the hypothesis that the transaction specific asset will improve the benevolence and that benevolence will improve the intention to continue the transaction were rejected and all the other 9 hypotheses were adopted include 2 moderating hypothesis. Conclusions - This study shows which dimension of trust suppliers should appeal to the buyer according to the uncertainty of the technology environment in order to maintain the transaction with the buyer. competence and integrity are important when technology environment uncertainty is low, and competence and benevolence are important when technical environment uncertainty is high. In order to improve competence, corporate reputation and transaction-specific asset are important. To improve integrity, corporate reputation and customer-linking capability are important. In order to improve benevolence, customer-linking capability is important. And various implications were discussed.

• The Korean Journal of Financial Management
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• v.24 no.3
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• pp.153-186
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• 2007

It is well known that the distributional properties of financial asset returns exhibit fatter-tails and skewer-mean than the assumption of normal distribution. The correct assumption of return distribution might improve the estimated performance of the Value-at-Risk(VaR) models in financial markets. In this paper, we estimate and compare the VaR performance using the RiskMetrics, GARCH and FIGARCH models based on the normal and skewed-Student-t distributions in two daily returns of the Korean Composite Stock Index(KOSPI) and Korean Won-US Dollar(KRW-USD) exchange rate. We also perform the expected shortfall to assess the size of expected loss in terms of the estimation of the empirical failure rate. From the results of empirical VaR analysis, it is found that the presence of long memory in the volatility of sample returns is not an important in estimating an accurate VaR performance. However, it is more important to consider a model with skewed-Student-t distribution innovation in determining better VaR. In short, the appropriate assumption of return distribution provides more accurate VaR models for the portfolio managers and investors.

• Steel and Composite Structures
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• v.39 no.3
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• pp.261-274
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• 2021

In a railway bridge, the CRTS II slab ballastless track is subjected to interlayer connection failures, such as void under slab, mortar debonding, and fastener fracture. This study investigates the influences of interlayer connection failure on the safe operation of high-speed trains. First, a train-track-bridge coupled vibration model and a bridge-track deformation model are established to study the running safety of a train passing a deformed bridge with interlayer connection failure. For each type of the interlayer connection failure, the effects of the failure locations and ranges on the track irregularity are studied using the deformation model. Under additional bridge deformation, the effects of interlayer connection failure on the dynamic responses of the train are investigated by using the track irregularity as the excitation to the vibration model. Finally, parametric studies are conducted to determine the thresholds of additional bridge deformations considering interlayer connection failure. Results show that the interlayer connection failure significantly affects the running safety of high-speed train and must be considered in determining the safety thresholds of additional bridge deformation in the asset management of high-speed railway bridges.