• Journal of the Korean Dietetic Association
• /
• v.29 no.2
• /
• pp.73-85
• /
• 2023

This study examined the nutritional information using the nutrition labels of gimbap sold at convenience stores and evaluated nutritional adequacy compared to dietary reference intakes for Korean adolescents. Thirty gimbaps (triangular gimbaps and regular gimbaps according to the main ingredients of vegetables, fish, and meat) were purchased at five convenience stores of different brands with many stores in Korea. The food and nutrition labels of the gimbaps were investigated, and nine minerals were analyzed using inductively coupled plasma-mass spectrometry (ICP-MS). The average price of gimbap was 1,906.7 won, and average energy was 292.0 kcal, and the protein content was 15.5% of the recommended intake for Korean male adolescents aged 15~18 years. The mineral content ranged from 6.9% for zinc to 39.0% for selenium. Except for sodium and selenium, the energy, protein, and mineral content did not meet 1/3 of dietary reference intakes for adolescents. For the index of nutritional quality (INQ), calcium and zinc were the lowest in the triangular and regular gimbap, respectively. The INQ of potassium was significantly higher in triangular gimbap with vegetables. The content and INQ of selenium were in regular gimbap with fish, and the zinc INQ was in regular gimbap with meat. Overall, gimbap sold in convenience stores has a high sodium content, and the contents of energy, protein, and minerals, except selenium, are insufficient for a single meal.

• International Journal of Computer Science & Network Security
• /
• v.23 no.9
• /
• pp.1-7
• /
• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.210-216
• /
• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Economic Analysis
• /
• v.26 no.3
• /
• pp.1-47
• /
• 2020

This paper surveys research since the 1990s on the ability of the yield spread and its components (i.e., expectation spread and term premium components) for future economic activity, and also conducts an empirical analysis of their forecasting ability using the yield data of Korean government bonds. This paper's survey, particularly for the US, shows that the yield spread has significant predictive power for some macroeconomic variables, but since the mid-1980s, its predictive power seems to have declined, possibly due to stronger inflation targeting. Next, this paper's empirical analysis using Korean data indicates that the yield spread, and the term premium component in particular, has significant predictive power for industrial production (IP) growth, consumer price index growth, and the IP gap. An out-of-sample analysis shows that the prediction equations are unstable over time, and that in predicting IP growth, the yield spread decomposition makes a significant contribution to the prediction of IP growth.

• Journal of Korean Society for Quality Management
• /
• v.51 no.2
• /
• pp.171-184
• /
• 2023

Purpose: First, this paper suggests an alternative approach to find optimal portfolio (stocks, bonds and ESG stocks) under the maximizing utility of investors. Second, we include ESG stocks in our optimal portfolio, and compare improvement of welfares in the case with and without ESG stocks in portfolio. Methods: Our main method of analysis follows Brennan et al(2002), designed under the continuous time framework. We assume that the dynamics of stock price follow the Geometric Brownian Motion (GBM) while the short rate have the Vasicek model. For the utility function of investors, we use the Power Utility Function, which commonly used in financial studies. The optimal portfolio and welfares are derived in the partial equilibrium. The parameters are estimated by using Kalman filter and ordinary least square method. Results: During the overall analysis period, the portfolio including ESG, did not show clear welfare improvement. In 2017, it has slightly exceeded this benchmark 1, showing the possibility of improvement, but the ESG stocks we selected have not strongly shown statistically significant welfare improvement results. This paper showed that the factors affecting optimal asset allocation and welfare improvement were different each other. We also found that the proportion of optimal asset allocation was affected by factors such as asset return, volatility, and inverse correlation between stocks and bonds, similar to traditional financial theory. Conclusion: The portfolio with ESG investment did not show significant results in welfare improvement is due to that 1) the KRX ESG Leaders 150 selected in our study is an index based on ESG integrated scores, which are designed to affect stability rather than profitability. And 2) Korea has a short history of ESG investment. During the limited analysis period, the performance of stock-related assets was inferior to bond assets at the time of the interest rate drop.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.10
• /
• pp.217-227
• /
• 2023

This study aims to stratify consumer market evaluation items from the Consumer Decision Journey(CDJ) perspective and understand the relationship between laws/systems and consumer orientation through the Korea Consumer Agency's '19 Korea Consumer Markets Evaluation Indicators. This study divided consumer market evaluation items into the selection comparison stage, selection decision stage, and post-purchase experience stage. And present a model that stratified the relationship with consumer orientation of laws/systems and verified using the CDJ model's experience as a control variable. Studies have shown that the relationship between the consumer market evaluation index that evaluates consumer orientation can be stratified according to the consumer decision-making stage and positively affects the relationship with consumer orientation of laws/systems. In addition, the impact of consumer market evaluation variables (reliability, and price) on the consumer orientation of laws/systems was different depending on the presence or absence of consumer damage experience.

• Korea Trade Review
• /
• v.47 no.4
• /
• pp.215-231
• /
• 2022

Analysis and forecasting of the Baltic Capsize Index (BCI) is important for managing an entity's losses and risks from the uncertainty and volatility of the fast-changing maritime transport market in the future. This study conducted volatility transition analysis through the GARCH model, using BCI which is highly related to steel raw materials. As for the data, 2,385 monthly data were used from March 1999 to March 2021. In this study, after basic statistical analysis, unit root and cointegration test, the GARCH, EGARCH, and DCC-GARCH models were used for volatility transition analysis. As the results of GARCH and EGARCH model, we confirmed that all variables had no autocorrelation between the standardized residuals for error terms and the square of residuals, that the variability of all variables at this time was likely to persist in the future, and that the variability of the time-series error term impact according to Iron ore trade (IoT). In addition, through the EGARCH model, the magnitude convenience of all variables except the Iron ore price (IOP) and Capesize bulk fleet (BCF) variables was greater than the positive value (+). As a result of analyzing the DCC-GARCH (1,1) model, partial linear combinations were confirmed over the entire period. Estimating the effect of variability transition on BCF and C5 with statistically significant linear combinations with BCI confirmed that the impact of BCF on BCI was greater than the impact of BCI itself.

• Korea Trade Review
• /
• v.46 no.6
• /
• pp.73-92
• /
• 2021

Small and medium-sized enterprises (SMEs) account for a large proportion of the total number of enterprises in many countries. The development of SMEs has contributed to job creation and economic benefits. Every government has formulated active diversification strategies to promote the export market of SMEs, but the performance of export capabilities remains insufficient. The primary purpose of this study is to examine the effectiveness of export credit insurance in promoting SME export performance in Canada. Using data from 2008-2017, the augmented Dickey-Fuller (ADF) model to test the stationarity of the concerned variables and the error correction model (ECM) and autoregressive distributed lag (ARDL) cointegration test to empirically investigate the cointegration relationship between the research targets. The results represent the positive and critical impact of export relative price and domestic demand pressure on Canada's export performance, and the negative impact of the export volume index at a significant level. Regrettably, the impact of export credit insurance on the export performance of Canadian SMEs is considered exaggerated overall. In view of this result, it is necessary for the Canadian government to enact policies based on the current market status. And enhance confidence among SMEs to begin exports and diversify their markets rather than focusing only on the domestic or US market, especially given the impact of COVID-19. From the case of Canada, Korean government can attempt to learn from them to conduct more efficient strategies for SMEs.

• Journal of Korea Trade
• /
• v.27 no.5
• /
• pp.167-198
• /
• 2023

Purpose - This paper aims to compare and analyze the co-movements and information spillover effects between the international commodity futures markets and the South Korean stock markets during the COVID-19 and the 2008 financial crises. Design/methodology - The DCC-GARCH model is used in the co-movements analysis. In contrast, the BEKK-GARCH model is used to evaluate information spillover effects. The statistical data used is from January 1, 2005, to December 31, 2022. It comprises the Korea Composite Stock Price Index data and daily international commodity futures prices of natural gas, West Texas Intermediate crude oil, gold, silver, copper, nickel, soybean, and wheat. Findings - The results of the co-movement analysis were as follows: First, it was shown that the co-movements between the international commodity futures markets and the South Korean stock markets were temporarily strengthened when the COVID-19 and 2008 financial crises occurred. Second, the South Korean stock markets were shown to have high correlations with the copper, nickel, and crude oil futures markets. The results of the information spillover effects analysis are as follows: First, before the 2008 financial crisis, four commodity futures markets (natural gas, gold, copper, and wheat) were shown to be in two-way leading relationships with the South Korean stock markets. In contrast, seven commodity futures markets, except for the natural gas futures market, were shown to be in two-way leading relationships with the South Korean stock markets after the financial crisis. Second, before the COVID-19 crisis, most international commodity futures markets, excluding natural gas and crude oil future markets, were shown to have led the South Korean stock markets in one direction. Third, it was revealed that after the COVID-19 crisis, the connections between the South Korean stock markets and the international commodity futures markets, except for natural gas, crude oil, and gold, were completely severed. Originality/value - Useful information for portfolio strategy establishment can be provided to investors through the results of this study. In addition, it is judged that financial policy authorities can utilize the results as data for efficient regulation of the financial market and policy establishment.

• The Korean Journal of Franchise Management
• /
• v.14 no.4
• /
• pp.35-50
• /
• 2023

Purpose: This study aims to uncover the mechanism of how initial fees are determined in the restaurant franchise business. Since the initial fees can be considered as a price of utilizing business models and operational knowledge of a certain franchise brand, it is critical to understand the fee decision-making process based on the strategic pricing theories. Therefore, this study investigates the influence of operational value on the determination of initial franchise fees grounded on a value-based pricing strategy. The Operational value is specifically categorized into profitability, growth, and stability of the franchise system. Research design, data, and methodology: The data used were collected through franchise disclosure documents and brand equity index provided by Korea Management Association Consulting. Data from 44 franchise restaurants during 2018 to 2021 are included in the sample. The panel dataset was analyzed by using generalized least squares estimation with R-Studio. Results: Profitability and stability positively influence initial franchise fees. However, growth did not influence initial franchise fees. Conclusions: The results of the study demonstrate that the operational value plays a critical role in determining the franchise fees. Specifically, franchisees recognize how much revenue a franchise system generates for them (i.e., profitability) and how stable the entire system is for operating business (i.e., stability) when they make purchasing decisions for franchise. The findings extend the pricing literature by applying pricing theories in the franchise fee context. Also, the study contributes to franchising and restaurant management literature by providing knowledge of how franchise fees are determined.