• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.37-50
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• 2020

Information security has become an important issue in the world. Various information and communication technologies, such as the Internet of Things, big data, cloud, and artificial intelligence, are developing, and the need for information security is increasing. Although the necessity of information security is expanding according to the development of information and communication technology, interest in information security investment is insufficient. In general, measuring the effect of information security investment is difficult, so appropriate investment is not being practice, and organizations are decreasing their information security investment. In addition, since the types and specification of information security measures are diverse, it is difficult to compare and evaluate the information security countermeasures objectively, and there is a lack of decision-making methods about information security investment. To develop the organization, policies and decisions related to information security are essential, and measuring the effect of information security investment is necessary. Therefore, this study proposes a method of constructing an investment portfolio for information security measures using game theory and derives an optimal defence probability. Using the two-person game model, the information security manager and the attacker are assumed to be the game players, and the information security countermeasures and information security threats are assumed as the strategy of the players, respectively. A zero-sum game that the sum of the players' payoffs is zero is assumed, and we derive a solution of a mixed strategy game in which a strategy is selected according to probability distribution among strategies. In the real world, there are various types of information security threats exist, so multiple information security measures should be considered to maintain the appropriate information security level of information systems. We assume that the defence ratio of the information security countermeasures is known, and we derive the optimal solution of the mixed strategy game using linear programming. The contributions of this study are as follows. First, we conduct analysis using real performance data of information security measures. Information security managers of organizations can use the methodology suggested in this study to make practical decisions when establishing investment portfolio for information security countermeasures. Second, the investment weight of information security countermeasures is derived. Since we derive the weight of each information security measure, not just whether or not information security measures have been invested, it is easy to construct an information security investment portfolio in a situation where investment decisions need to be made in consideration of a number of information security countermeasures. Finally, it is possible to find the optimal defence probability after constructing an investment portfolio of information security countermeasures. The information security managers of organizations can measure the specific investment effect by drawing out information security countermeasures that fit the organization's information security investment budget. Also, numerical examples are presented and computational results are analyzed. Based on the performance of various information security countermeasures: Firewall, IPS, and Antivirus, data related to information security measures are collected to construct a portfolio of information security countermeasures. The defence ratio of the information security countermeasures is created using a uniform distribution, and a coverage of performance is derived based on the report of each information security countermeasure. According to numerical examples that considered Firewall, IPS, and Antivirus as information security countermeasures, the investment weights of Firewall, IPS, and Antivirus are optimized to 60.74%, 39.26%, and 0%, respectively. The result shows that the defence probability of the organization is maximized to 83.87%. When the methodology and examples of this study are used in practice, information security managers can consider various types of information security measures, and the appropriate investment level of each measure can be reflected in the organization's budget.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.19-26
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• 2023

The impact of environmental, social and governance (ESG) performance on investors' decision-making is growing. Investors' focus on the financial performance of firms in the past is expanding to the non-financial performance of the interests of stakeholders surrounding firms. Against this backdrop, this study conducted a panel regression analysis on firms evaluated by Korea Corporate Governance Service to analyze the impact of ESG performance, a firm's non-financial performance, on firm risk. According to the analysis, ESG performance has a negative (-) effect on all three firm risks (systematic risk, unsystematic risk, and total risk), indicating that the stakeholder theory and risk management theory are supported. The implications of this study are: First, ESG reduces not only unsystematic risk but also broad and indiscriminate systematic risk; Second, investors can reduce the risk of their investment portfolio by executing ESG investments; Third, companies can achieve stable financial performance even in adverse circumstances by utilizing the insurance function of ESG management; Lastly, the government can enhance the stability of the financial market while improving the financial soundness of firms through reasonable ESG-related regulations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.241-250
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• 2023

Companies are paying attention to project governance to realize their goal strategies, project success, and project value in a rapid and complex environment due to the development of information technology. This study aims to explore project governance and factors from literature review and practice guides related to project governance and empirically analyze the impact of project governance factors on IT project objective performance. The survey was conducted on project managers, project team members, and project management members with experience in carrying out business IT projects within two years at companies in various industries in Korea. The main conclusion is as follows. Among the project governance factors, disclosure and reporting that uses and reports using appropriate and reliable information about projects related to decision-making activities positively impact the achievement of IT project objectives: business success and stakeholder satisfaction. Among the project governance factors, project sponsorship, a role that effectively connects management and projects, and a project management framework that provides tools, procedures, resources, and techniques for projects positively impact IT project objective performance. This study is of academic and practical significance in that it identifies the definition and factors of project governance through companies in various industries in Korea and the positive impact of project governance factors on IT project objective performance.

• 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 Technology Innovation
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• v.27 no.2
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• pp.1-36
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• 2019

In line with the international community's movement to reduce greenhouse gas emission, Korea implemented FIT(Feed in Tariff) in 2002 as part of its renewable energy development project. Although the policy had shifted to full-scale RPS(Renewable Portfolio Standards) in 2012, policymakers are still seeking changes due to policy ineffectiveness. While previous studies explain sudden policy changes through external factors, recent research sheds light on internal factors in the process of policy transition. The purpose of this study is to investigate the factors that are responsible for rapidly changing policies in latecomer nations. In order to find this, we look at the case of transition from the FIT to the RPS in Korea's expansion of renewable energy policy. As a result of the research, it is confirmed that the Top-Down decision making system of Korea and the external regulatory change cause rapid policy transition. By looking at these variables, we propose useful implications for policymakers to minimize the policy failure in future policy design and evolution.

• Korean small business review
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• v.41 no.1
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• pp.29-51
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• 2019

This study investigates how SMEs' (small and medium-sized enterprises) financing strategies affect firm valuation. Given that information asymmetry is engaged in firm valuation in the stock market, investors interpret the meanings of debt financing depending on how SMEs construct the portfolio of financing strategies (retained earnings vs debt financing), thereby making investment decision. Specifically, given that SMEs' debt financing has two meanings in the market signals, called "benefit" and "cost", this study postulates that firm valuation will be differently made by investors, depending on how they interpret the meanings of debt financing under choice competition between retained earnings and debt financing. In this study, we argue that under choice competition, as a SME's debt proportion increases, the "cost" signal outweighes the "benefit" signal, thereby decreasing firm valuation. Moreover, the effect of such signal can be contingent on the SME's characteristics-firm visibility. These ideas are examined using 363 U.S. SMEs ranging from 1971 to 2010. The fixed-effects models estimating Tobin's q show that under choice competition, a SME's debt proportion has a negative impact on firm valuation and that the firm's high visibility mitigates the effect of "cost" signal. In conclusion, this study sheds new light on how investors' interpretations of SMEs' financing strategies affect firm valuation.

• 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.