• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• Korea Trade Review
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• v.41 no.2
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• pp.203-229
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• 2016

Korean general trading companies had started their business to benchmark and to adopt the successful new business models of the Japanese ones. Nevertheless, the strategic gaps between Korean and Japanese GTCs, Sogo-Shosha, still exist, including financial profitability and managerial competencies. In this regard, it is academically and practically required to find out the differences between Korean and Japanese GTCs. This study overviews the previous researches and the business cases to understand the features of GTCs and to get recent and meaningful factors which are related with the rebirth of Sogo-Shosha. Thereafter, in-depth interviews with industry experts and scholars and subsequent investigations were also conducted to suggest meaningful implications for both academicians and practitioners with the found factors. This study suggests four fundamental differences between the Korean and Japanese GTCs: ① the origin and growth path, ② business ownership, governance strategies and contracts management, ③ availabilities of investment portfolio and risk management, ④ business operation system and organizational culture. This study suggests meaningful implications for Korean GTCs to apply the experiences and lessons learned from Japanese Sogo-Shosha into their own business.

• Design Convergence Study
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• v.16 no.4
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• pp.1-16
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• 2017

As the local manufacturing industry has entered a phase of stagnation, service and product design based on user experience has been highlighted as an alternative for the innovation. However, SMEs(Small and Medium-sized Enterprises) are still struggling to overcome the current crisis. One of the reasons is that SMEs do not have enough contact points with the validated UX firms and experts. Thus, SMEs has a high barrier to invest in new opportunity area, user experience. In this study, we aim to figure out UX experts' competence to perform the UX method to solve the UX problems based on the KSA framework(Knowledge, Skill, Attitude). Based on the literature review and expert workshop, we grouped the UX method according to the similarity of the competence required to conduct the method. With cluster analysis, 5 different groups of UX method were defined based on the competence, Panoramic Analysis, Meticulous Observation and Analysis, Intuitive Interpretation, Agile Visualization, and Logical Inspection. The results would be applied to compose a portfolio of UX experts and to implement a mechanism that could recommend the professional experts to the company.

• Asia Marketing Journal
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• v.12 no.4
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• pp.183-201
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• 2011

In case of Aju capital, it adopted a strategy to use a single brand not two separate brands after M&A was completed. In order to implement this strategy, it has endeavored to effectively process the work of shifting existing marketing infrastructure of DAEWOO capital, the mergee, spending enough transition time for the brand migration. In the process of merging, Aju capital picked the strategy to use the brand of mergee first, which is the Daewoo Capital brand, and then took a transition time for a while to converge to the single brand of Aju capital. Putting another way, even if the M&A deal was completed back in 2005, it maximized the effect of launching its final brand "Aju capital" by capitalizing on the positive image of "Daewoo" during the transition time and changing its name just in the right moment. In a bid to implement this strategy successfully, it established a cautious but sophisticated brand migration strategy. 1) "Brand bridge" strategy through reinforcing brand power of "Naegeron", which is an individual product brand of Daewoo Capital 2) Establishing a good brand image through reinforcing customer satisfaction 3) It implemented and completed its brand transition initiative by going through the step of Aju Capital brand unification (from Sept 09 to present) Currently, the sales unit of Aju Capital is realizing quality growth through specialization. It's strategy is to construct a systematic sales portfolio in terms of both quality and quantity through product-by-product specialization where the existing practice was selling a variety of products in a single branch. Back in 2009, it opened a branch that specialize in imported cars and expanded its used car business to 6 specialized locations. Besides, the specialized locations for personal loan named "Naegeron" was expanded from 3 to 11 locations. Recently, it is expected that it will inject vigor to retail and corporate financing business alongside with its core business, which is auto financing.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.169-181
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• 2023

This study was conducted to examine the remuneration curriculum of vocational ability development training teachers and instructors and to examine ways to apply micro credentials. To this end, the current status of the remuneration curriculum of vocational ability development training instructors and instructors at K University's Competency Education Development Institute, the characteristics of micro credentials, and the possibility of linking the remuneration curriculum to micro credentials are as follows. First, most of the recognition of digital certificates was positive for digital certificates such as digital credit, digital badge issuance, and recognition of the recognized qualification process of maintenance education when completing the training course. In addition, as a method of applying micro credentials to conservative education, various cases were proposed to benefit from conservative education, systematization and grading of the qualification process, and credit of the qualification process. Second, as an institutional supplement to enhance the utilization of conservative education using micro credentials, the need to expand NCS-based major conservative education, provide efficient learning contents and learning methods, and set minimum completion time. In addition, the most common response as a way to improve the understanding of teachers and instructors in vocational ability development training was the micro credential promotion plan. Third, in the role of conservative education institutions and vocational ability development training instructors and instructors, conservative education institutions mention maintaining educational quality the most, and active participation was the role of vocational ability development training instructors. Through this study, it is expected to establish a vocational training environment that can enhance expertise and provide a practical portfolio of practical competency history by linking the remuneration curriculum of vocational competency development training instructors and micro credentials.

• Information Systems Review
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• v.25 no.4
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• pp.27-45
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• 2023

The importance of information security has grown alongside the development of information and communication technology. However, companies struggle to select suitable countermeasures within their limited budgets. Sönmez and Kılıç (2021) proposed a model using AHP and mixed integer programming to determine the optimal investment combination for mitigating information security breaches. However, their model had limitations: 1) a lack of objective measurement for countermeasure efficacy against security threats, 2) unrealistic scenarios where risk reduction surpassed pre-investment levels, and 3) cost duplication when using a single countermeasure for multiple threats. This paper enhances the model by objectively quantifying countermeasure efficacy using the beta probability distribution. It also resolves unrealistic scenarios and the issue of duplicating investments for a single countermeasure. An empirical analysis was conducted on domestic SMEs to determine investment budgets and risk levels. The improved model outperformed Sönmez and Kılıç's (2021) optimization model. By employing the proposed effectiveness measurement approach, difficulty to evaluate countermeasures can be quantified. Utilizing the improved optimization model allows for deriving an optimal investment portfolio for each countermeasure within a fixed budget, considering information security costs, quantities, and effectiveness. This aids in securing the information security budget and effectively addressing information security threats.

• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.69-76
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• 2023

To improve the financial stability of the National Pension, an appropriate target rate of return should be established based on pension liabilities, and asset allocation policies should be formulated accordingly. The purpose of this study is to calculate the target rate of return considering the contributions of subscribers and the pension benefits, and based on this, derive an asset allocation. To do this, we utilized the internal rate of return methodology to calculate the target rate of return for each cohort. And then, we employed a Monte Carlo simulation-based re-sampling mean-variance model to derive asset allocation for each cohort that satisfy the target rate of return while minimizing risks. Our result shows that the target rate of return for each cohort ranged from 6.4% to 6.85%, and it decreased as the generations advanced due to a decrease in the income replacement rate of the National Pension. Consequently, the allocation of risky assets, such as stocks, was relatively reduced in the portfolios of future generations. This study holds significance in that it departs from the macroeconomic-based asset allocation methodology and proposes investments from an asset-liability management perspective, which considers the characteristics of subscribers' liabilities.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.

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

• Journal of Service Research and Studies
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• v.5 no.1
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• pp.91-103
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• 2015

We examine the information transmission between the KT Spot and the KT Futures Index, the SK Telecom Spot and the SK Telecom Futures Index, based on the returns data offered by the Korea Exchange. The data includes daily return data from 1 January 2012 to 31 December 2014. Utilizing a dynamic analytical tool-the VAR model, Granger Causality test, Impulse Response Function and Variance Decomposition have been implemented. The results of the analysis are as follows. Firstly, results of Granger Causality test suggests the existence of mutual causality the KT Futures Index and the SK Telecom Futures Index precede and have explanatory power the KT Spot and the SK Telecom Spot However the results also identified a greater causality and explanatory power of the KT Spot and the SK Telecom Spot over the KT Futures Index and the SK Telecom Futures Index. Secondly, the results of impulse response function suggest that the KT Futures Index show immediate response to the KT Spot and are influenced by till time 4. From time 2, the impact gradually disappears. Also the SKT Futures Index show immediate response to the SKT Spot and are influenced by till time 4. From time 2, the impact gradually disappears. Lastly, the variance decomposition analysis shows that the changes of return of the KT Spot and SKT Spot are dependent on those of the KT Futures Index and the SK Telecom Futures Index. This implies that returns on the KT Spot and SKT Spot have a significant influence over returns on the KT Futures Index and the SK Telecom Futures Index. It contributes to the understanding of market price formation function through analysis of detached the KT Spot and the KT Futures Index, the SK Telecom Spot and the SK Telecom Futures Index.