• Journal of Digital Convergence
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• v.22 no.2
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• pp.1-9
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• 2024

The purpose of this study is to verify whether companies residing in industry-academic convergence zones designated by the government are achieving policy goals and to seek policy implications and directions for improvement through analysis. For the study, business activities targeting resident companies were divided into infrastructure, business content, management, and system aspects, and business performance indicators, resident company satisfaction surveys, and differences in sales increase between resident companies and non-resident companies were analyzed through t-test. Based on statistical analysis results, performance indicators, and corporate survey analysis results, we track joint industry-academia R&D projects to maximize the effectiveness for companies, develop and operate human resources management for teams, and provide financial support for ordinances of metropolitan local governments. Improvements such as stipulation, antenna facilities at the corporate research center, and improvement of the researcher's residential environment were suggested. This study is the first to quantitatively verify policy performance targeting companies residing in industry-academic convergence zones, a large-scale government project, and future follow-up research is needed, including analysis of policy effects based on various variables such as employment indicators and corporate financial indicators.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.119-130
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• 2011

This paper measures and analyzes the performance of insurance companies in Korea in respect to sustainable development and suggest strategic implications based on the analysis. The correlation, regression, ANOVA, and t-test are employed. The results of this study are summarized as follows. First, it shows tat social index is important in the life insurance industry; however, the environmental index, is important in the non-life insurance industry. Second, the result gained by regressing the size and financial soundness on the performance of sustainable development demonstrates that the size variable is statistically significant. It suggests that size is a necessary condition for sustainable development. Finally, ANOVA shows that the small and medium sized companies have a significantly poor performance compared to the large companies concerning the social index and reputation index in the life insurance industry. The small and medium sized companies in the non-life insurance industry exhibit a significantly poor performance compared to the large companies in respect to all the indexes, except for the social index. Therefore, the small and medium sized companies make every endeavor in the poor indexes to improve performance.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Management Science and Financial Engineering
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• v.14 no.1
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• pp.23-34
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• 2008

In this study, we consider stationary waiting times in a Poisson driven single-server m-node queues in series. We assume that service times at nodes are independent, and are either deterministic or non-overlapped. Each node excluding the first node has a finite waiting line and every node is operated under a FIFO service discipline and a communication blocking policy (blocking before service). By applying (max, +)-algebra to a corresponding stochastic event graph, a special case of timed Petri nets, we derive the explicit expressions for stationary waiting times at all areas, which are functions of finite buffer capacities. These expressions allow us to compute the performance measures of interest such as mean, higher moments, or tail probability of waiting time. Moreover, as applications of these results, we introduce optimization problems which determine either the biggest arrival rate or the smallest buffer capacities satisfying probabilistic constraints on waiting times. These results can be also applied to bounds of waiting times in more general systems. Numerical examples are also provided.

• 한국전산유체공학회:학술대회논문집
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• 2007.04a
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• pp.153-158
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• 2007

Since the introduction of Nanoimprint in the mid-1990s, Nanoimprint lithography, a low-cost, non-convential method, has been the dominant lithography technology that guarantees high-throughput patterning of nanostructures. Based on the mechanical embossing mechanism, Nanoimprint lithography creates the nanopatterns on the polymer material cast on the substrate. In essence, the process needs nanofabrication equipment for printing with the adequate control of temperature, pressure and control of parallels of the stamp and substrate. This article introduce the possibility and reality of the thermal control on the hot plate using a CFD code. Numerical computation has been conducted for assessing the feasibility of a hot plate($120{\times}120\;mm2$). PID control is adopted to ensure high temperature uniformity in several zones. Parallel experiments have also been performed for verifying thermal performance. Not only show the results the optimum number of thermocouples related to controllers but also suggest that the thermal simulation using a CFD code would be an alternative method to design and develop the thermal control equipment in the financial aspect.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.527-538
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• 2021

The purpose of the study is to determine the efficiency, position, and partner selection of securities companies via the negative super-SBM model used in data envelopment analysis (DEA). This model utilizes a variety of inputs, including current assets, non-current assets, fixed assets, liabilities, owner's equity and charter capital, and outputs including net revenue, gross profit, operating profit, and net profit after tax collected from the financial reports (Vietstock, 2020) of 32 securities companies, operating during the period from 2016 to 2019, negative data are collected as well. Empirical results determined both efficient and inefficient terms, and then further determined the position of each securities firm under consideration of every term. The overall score arrived at discovered a large performance change realizing a maximum score able to reach 20.791. In the next stage, alliancing inefficient companies was carried out based on the 2019 scores to seek out optimal partners for the inefficient companies. The tested result indicated that AAS was the best partner selection when its partners received a good result after alliancing, as with FTS (11.04469). The partner selection is deemed as a solution helpful to inefficient securities companies in order to improve their future efficiency scores.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.741-750
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• 2021

The research aims to analyze the level of efficiency by grouping banks during the period 2017 - 2018 into category 1 and category 2 banks and then dividing them as Regional Development Banks (BPD) and Non-BPD Conventional Commercial Banks (BUK) within each category. The research objects are banks within the categories BPD and BUK comprised 18 BPDs and 35 BUKs. The research methodology uses 3 stages, first, using Data Envelopment Analysis (DEA) we measure the level of bank efficiency; second, using the Tobit regression model we evaluate the effect of financial performance on DEA efficiency, and third, using the Mann-Whitney test we determine whether there is a difference in the efficiency of category 1 and 2 banks. The results showed that there was a decrease in the efficiency of category 1 and 2 banks but on average, the efficiency of category 1 banks is higher than category 2 banks. The estimation results of the Tobit regression model show that only the ROA variable affects the efficiency level of category 1 banks, while category 2 banks are influenced by NPL and ROA variables. In the Mann-Whitney test, it was proven that there were differences in efficiency between BUK and BPD in category 1 and 2 banks.

• The Journal of Economics, Marketing and Management
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• v.10 no.3
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• pp.21-36
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• 2022

Purpose: The purpose of this study is to compare and analyze the enterprise's score index calculated from atypical data and corrected data. Research design, data, and methodology: In this study, news articles which are non-financial information but qualitative data were collected from 2,432 SMEs that has been extracted "square proportional stratification" out of 18,910 enterprises with fixed data and compared/analyzed each enterprise's score index through text mining analysis methodology. Result: The analysis showed that qualitative data can be quantitatively evaluated by region, industry and period by collecting news from SMEs, and that there are concerns that it could be an element of alternative credit evaluation. Conclusion: News data cannot be collected even if one of the small businesses is self-employed or small businesses has little or no news coverage. Data normalization or standardization should be considered to overcome the difference in scores due to the amount of reference. Furthermore, since keyword sentiment analysis may have different results depending on the researcher's point of view, it is also necessary to consider deep learning sentiment analysis, which is conducted by sentence.

• Journal of Korea Trade
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• v.23 no.6
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• pp.93-112
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• 2019

Purpose - First, this study contextually examines the governance codes of South Africa and South Korea. Second, it analyzes board features of South African (JSE) Mainboard and South Korean (KRX) KOSPI-listed companies. Design/methodology - This review is qualitative and uses data from the annual reports of the selected markets' companies, respective exchanges' official web sites and corporate governance-related web sites in order to examine the corporate governance practices in the two markets. In addition, Nvivo is employed in analyzing the content of the corporate governance codes of the selected countries. Findings - Our analysis indicates that the corporate governance codes of the two countries are evolving to keep up with the international trend of principles-based approach. The composition of the board of directors (BODs) of non-financial companies of both South Africa and South Korea shows no significant variation between the companies with regards to the executive (inside) and nonexecutive (outside) directors. On the contrary, there is a significant variation between South African and South Korean listed companies with respect to diversity. Originality/value - While previous studies are centered on the impact of governance codes on performance, this study intends to contextually evaluate the codes and features of South Africa and South Korea listed companies. This is essential and timely for regulators and policy makers given the importance of corporate governance features such as board independence and diversity in recent times.

• Journal of Distribution Science
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• v.18 no.3
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• pp.77-85
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• 2020

Purpose: This study examined marketing value as online word-of-mouth media in the foodservice industry, and it did research on online word-of-mouth (e-WOM) communication marketing schemes using mass communication in the industry. The study is also intended to investigate the impact of electronic word-of-mouth (e-WOM) information and communication on product awareness risks, benefits, and word-of-mouth (WOM) impacts on restaurant consumers. Research design, data, and methodology: The analysis was conducted on a valid questionnaire of 425 menu product consumers. The survey was conducted for two months in March 2019. The collected data was analyzed using SPSS and hierarchical regression analysis was applied. Results: It did empirical research on the reciprocal casual relations to online and the existing word-of-mouth communication that have to be preceded to understand characteristics of online word-of-mouth communication for the purpose of this study. The result is summarized as follows. First, the online word-of-mouth (e-WOM) effect on product recognition risk shows the statistically significant effect of information sender characteristics, information recipient characteristics, and online word-of-mouth (e-WOM) communication on product recognition risk. Second, the influence of online word-of-mouth (e-WOM) on product risk benefits shows that the information sender characteristics, the information receiver characteristics, and online communications have a statistically significant effect on product risk benefits. Third, online word of mouth risk recognition had a statistically significant effect on word of mouth acceptance. Fourth, online risk benefit had a statistically significant positive effect on word of mouth (WOM) effect. Conclusions: The communication between online word of mouth (e-WOM) sender and recipient had a positive influence on the product evaluation and attitude change in the foodservice industry, and the word-of-mouth (WOM) effect affected financial and non-financial performance. The results mentioned above indicated that the communication between the sender of the information and the receiver of the information had a positive effect on the product evaluation and attitude change of the menu consumer, and the word-of-mouth (WOM) result affected the financial. Therefore, the online word-of-mouth (e-WOM) effect has a positive effect on the word-of-mouth (WOM) effect of menu products when performed simultaneously and positively between the information sender and the information receiver.