• Journal of Korean Society for Quality Management
• /
• v.50 no.1
• /
• pp.153-167
• /
• 2022

Purpose: The purpose of this study is to guide the operation managers who plan daily production of large mass-processing facility that services multi-customers with multi-product, small-batch item characteristics by providing the practical best production quantity and the inventory allowed to build. Methods: Close observation of a subcontract paint-shop operator captured the daily decision process which was reflected in the subcontractor-unique mathematical model and the system dynamics simulation model. Multiple simulations were run to find the practical best production quantity and the maximum allowable stock level of inventory that did not undermine the profit from practical best daily production. Actual data and a few constant values were obtained from the firm under study. Results: While the inventory holding cost for the customer-owned material harms the total profit of the subcontractor, the running cost of the processing facility hinders production in small batches. This balances the maximum possible productions and results in practical best daily production which can be found through simulation runs with actual data. The maximum level of stocked inventory is deduced from the practical best daily production. Conclusion: To build a large volume that enables economy-of-scale production, operators should deal with multi-product small-batch items from multiple customers. When the planned schedule of the time and amount of material in-flow tend not to be reliable, operators can find it practical to execute level production across the planning horizon instead of adjusting to day-to-day in-flow fluctuations.

• Asia-Pacific Journal of Business
• /
• v.14 no.2
• /
• pp.51-61
• /
• 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 the Korea Society of Computer and Information
• /
• v.29 no.5
• /
• pp.11-20
• /
• 2024

This study presents TabNet, a novel deep learning method, to enhance corporate credit rating accuracy amidst growing financial market uncertainties due to technological advancements. By analyzing data from major Korean stock markets, the research constructs a credit rating prediction model using TabNet. Comparing it with traditional machine learning, TabNet proves superior, achieving a Precision of 0.884 and an F1 score of 0.895. It notably reduces misclassification of high-risk companies as low-risk, emphasizing its potential as a vital tool for financial institutions in credit risk management and decision-making.

• Journal of the Korea Safety Management & Science
• /
• v.26 no.1
• /
• pp.99-106
• /
• 2024

In the event of an emergency such as facility shutdown during process operation, the by-product gas must be urgently discharged to the vent stack to prevent leakage, fire, and explosion. At this time, the explosion drop value of the released by-product gas is calculated using ISO 10156 formula, which is 27.7 vol%. Therefore, it does not correspond to flammable gas because it is less than 13% of the explosion drop value, which is the standard for flammable gas defined by the Occupational Safety and Health Act, and since the explosion drop value is high, it can be seen that the risk of fire explosion is low even if it is discharged urgently with the vent stock. As a result of calculating the range of explosion hazard sites for hydrogen gas discharged to the Bent Stack according to KS C IEC 60079-10-1, 23 meters were calculated. Since hydrogen is lighter than air, electromechanical devices should not be installed within 23 meters of the upper portion of the Bent Stack, and if it is not possible, an explosion-proof electromechanical device suitable for type 1 of dangerous place should be installed. In addition, the height of the stack should be at least 5 meters so that the diffusion of by-product gas is facilitated in case of emergency discharge, and it should be installed so that there are no obstacles around it.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.107-122
• /
• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Management & Information Systems Review
• /
• v.29 no.4
• /
• pp.23-44
• /
• 2010

This study examines the value-relevance of accruals and discretionary accruals. Also, by examining the effects of the corporate life-cycle on these relationship, this study is able to provide evidence of the value-relevance of accruals and discretionary accruals measures in the economic context of life-cycle theory. This study uses results based on life-cycle classification methods developed by Anthony and Ramesh(1992), adjust Jones model and Dechow Dechev(2002) model. We classify firms using individuals variables(sales growth, capital expenditure growth, employee growth) and then use a composite score obtained from all variables for classification. Our sample consists of 272 firms listed in the Korean Stock Exchange during 14 years(1996-2009). Our final sample for regression variables consists of 2,448 firm-year observations. This evidence implies that the value-relevance of accruals and discretionary accruals in the growth and mature stage can have positive impact on the price but in the decline the value-relevance of accruals and discretionary accruals can have negative impact on the price. The results mean that discretionary accruals communicate managements' private information in the growth stage, but. earnings management in the decline stage. The results of this study suggest that corporate life cycle stages influence the value-relevance of accruals and discretionary accruals measures.

• The Korean Journal of Quaternary Research
• /
• v.18 no.2 s.23
• /
• pp.107-107
• /
• 2004

In this paper we have introduced some results of study on stand growth pattern and stand structure of larch forest which are located in selected forest sites of Khangai and Gobi-Altai mountain ranges of Mongolia. Our investigations showed that growth intensity and stand structure in western Mongolia are very specific from the other forest vegetation zones of Mongolia. Studies on the stand structure and growth trend indicate that tree types of stand structure and different types of growth of Larix sibrica are very common in Western Mongolia. These peculiarities of stand structure and growth of larch stands in Western Monolia could be used for inventory work and an improvement of the forest management in Western Mongolian region. The larch tree is the dominant tree species in Western Mogolia. Forest cover of the region is about 15%, which is two times higher than the country's average. In this region forest area is divided into 4 forest sub-regions: the Central Khangai, Western Khangai, North Eastern Khangai and South Easterun Khangai sub-regions including taiga, pseudo taiga, sub taiga, sub-alpine and forest steppe belts. Silviculture practices and forest research management request to study forest growth trends in local and general conditions, which means to indicate a change of taxonomic characteristics of stand from time to time including diameter, height, basal area, growth stock etc. The forest management practice mostly uses tables of forest growth and yield based on the results of long term research on forest growth. Forest yield tables and other relevant forest standards of Russia are used for the forest inventory and forest management. They are not able to determine forest structure and growth peculiatities of Mongolian forests. Studies on forest resource assessment in Mongolia indicate that after logging operations and forest fires the natural regeneration of desired species such as pine and larch often does not succeed. This situation forces to take a different approach of forest management and silviculture practice depending on the stand structure and growth rate of the forest stands. According to our investigation in last years, forest growth pattern of larch forest depends mostly on stand structure, stand age and growth condition including forest soil, climate and location in different slopes. Due to improve environmental function of forest ecosystem in the region, it is needed to conduct very comprehensive study of high mountain forest ecosystem in selected sub-regions.

• The Korean Journal of Financial Management
• /
• v.25 no.1
• /
• pp.109-140
• /
• 2008

The primary objective of this paper investigates whether asymmetric volatility phenomenon is caused by differences of opinion among investors and analyses information availability has an effect on asymmetric volatility. The empirical test period covers recent 6 years from January 4, 2000 to December 29, 2005. Five portfolios have been formed according to information availability(volume and market value). For the purpose of this study, We use TGARCH model, TGARCH-M model and adjusted model which include trading volume as a proxy differences of opinion among investors. The results are summarized as follows ; First, adjusted model analysis shows that asymmetric volatility phenomenon is disappeared or asymmetric coefficient and ratio is decreased than basis model. Second, portfolio analysis shows that the higher volume and market value, the more prominent asymmetric volatility phenomenon. And adjusted model analysis shows the higher volume and market value, the more decrease asymmetric ratio. Over all, assertion that differences of opinion among investors has caused asymmetric volatility phenomenon is regarded as reasonable. And, We see that information availability have great effect on asymmetric volatility phenomenon. We think that theses results can also occur opinion adjustment of optimistic investors. Namely, asymmetric volatility phenomenon can occur difference of information authenticity.

• Food Science of Animal Resources
• /
• v.30 no.5
• /
• pp.703-716
• /
• 2010

It has recently been considered urgent to establish an accurate definition and scope of functional foods in the livestock industry in Korea. The tertiary function of food is gradually emphasized with various changes in conditions such as international competitive power enhancement of the whole world, including Korea. Therefore, the purpose of this study was carried out to gather basic information toward the establishment of standardization, management plans, and a system adapted to the Korean state through researching management systems of functional meat and meat products in the European Union (EU) and the Oceania Area (OA). A brief summary of this study is as follows. In the EU and OA, special management systems and laws about functional livestock and products were once nonexistent; only 'nutrition claims' and 'health claims' operated in the nutritional consolidation side of foods. Also, it was once thought that functional index materials and permissions established in the application of functional products sufficed, because management standards for enriched food were not established. Therefore, standardization of functional live stock products needs to be considered based on the case of developed countries in the EU and OA. It also seems that the above functional indicative substances can be applied, based on normal standards of indicative substances of functional products and the normal standards of indicators of functional substances added to livestock products presented in this study.

• The Korean Journal of Financial Management
• /
• v.21 no.1
• /
• pp.33-58
• /
• 2004

This paper tests Van Home's hypothesis, a negative correlation between degrees of operating leverage(DOL) and financial leverage(DFL). For an empirical analysis, we extract information from financial statements of manufacturing companies listed in the Korea Stock Exchange. Data extend from 1980 to 2001. The DOL continued to increase until 1997, but decreased dramatically after the IMF financial crisis. However, the DOL has been at a higher level than companies of other countries such as USA and Japan. The DFL has been maintained at a much higher level, as expected. The empirical results indicate a positive correlation between the DOL and the DFL, which is inconsistent with the VanHorne's hypothesis. To further investigate, we divide the whole sample into subgroups according to such management elements as asset size, level of leverages, earnings and cash flow. The results for sub-samples are different from those of whole sample. This indicates we need to incorporate specific managerial factors in order to correctly explain financial decision processes.