• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.151-159
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• 2012

This paper proposes to develop multi-currencies portfolio strategy using principal component analysis (PCA) and logistic regression (LR) in foreign exchange market. While there is a great deal of literature about the analysis of exchange market, there is relatively little work on developing trading strategies in foreign exchange markets. There are two objectives in this paper. The first objective is to suggest portfolio allocation method by applying PCA. The other objective is to determine market timing which is the strategy of making buy or sell decision using LR. The results of this study show that proposed model is useful trading strategy in foreign exchange market and can be desirable solution which gives lots of investors an important investment information.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.209-235
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• 2022

Starting from 2010, blockchain technology, along with the development of artificial intelligence, has been in the spotlight as the latest technology to lead the 4th industrial revolution. Furthermore, previous research regarding blockchain's technological applications has been ongoing ever since. However, few studies have been examined the standards for classifying the blockchain economic ecosystem from a capital market perspective. Our study is classified into a collection of interviews of software developers, entrepreneurs, market participants and experts who use blockchain technology to utilize the blockchain economic ecosystem from a capital market perspective for investing in stocks, and case study methodologies of blockchain economic ecosystem according to application fields of blockchain technology. Additionally, as a way that can be used in connection with equity investment in the capital market, the blockchain economic ecosystem classification methodology was established to form an investment universe consisting of global blue-chip stocks. It also helped construct an intelligent portfolio through quantitative and qualitative analysis that are based on quant and artificial intelligence strategies and evaluate its performances. Lastly, it presented a successful investment strategy according to the growth of blockchain economic ecosystem. This study not only classifies and analyzes blockchain standardization as a blockchain economic ecosystem from a capital market, rather than a technical, point of view, but also constructs a portfolio that targets global blue-chip stocks while also developing strategies to achieve superior performances. This study provides insights that are fused with global equity investment from the perspectives of investment theory and the economy. Therefore, it has practical implications that can contribute to the development of capital markets.

• Science & Technology Policy
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• v.18 no.2
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• pp.88-102
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• 2008

• 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 Information Technology
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• v.3 no.3
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• pp.89-102
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• 2000

The purposes of this research are to analyze information contents of net book value and earnings. If investors use Net book value and earnings to their investment strategies, These informations are positively to be correlated with future corporate value. Research methodologies applied are theoretical and empirical ones. Results of the study are as follows: First, as investors form their investment portfolios, PBR and ROE are good indicator in finding out undervalued and overvalued corporation. Second, present earnings are differently reflected in future stock prices. Price-earnings correlation of undervalued corporations are higher than that of overvalued ones. Results of this study imply that Investors can utilize net book value and earnings to their investment strategies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.899-904
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• 2013

This paper proposes an efficient portfolio selection methodology for the listed construction corporations in KOSPI and KOSDAQ. For the construction industrial sector classified by KRX(Korea Exchange), the proposed method carries out an efficiency analysis using DEA (Data envelopment analysis) approach and for the efficient corporations filtered by DEA, construct portfolio using Markowitz's Model. In order to show the effectiveness of the proposed method, we constructed annually portfolios for 5 years (2007-2011) out of 53 listed corporations in KOSPI and KOSDAQ, and proved that our portfolios are superior to benchmark portfolios in terms of rate of returns.

• Management & Information Systems Review
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• v.33 no.5
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• pp.249-260
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• 2014

This paper explores the relationship between firm-specific volatility and some firm characteristics such as size, the market-to-book ratio of equity, PER, PBR, PCR, PSR and turnover in KOSDAQ market. In addition, I investigate whether portfolios with difference to realized range-based volatility and firm-specific volatility have different investment performance using CAPM and FF-3 factor model. The main findings of this study can be summarized as follows. First, firm-specific volatility have mostly positive relationship between firm-specific volatility and some firm characteristics. Second, this study found that realized range-based volatility and firm-specific volatility are positively related to expected return. It means that portfolios with high idiosyncratic volatility have significantly higher expected return than portfolios with low firm-specific volatility.

• The Journal of the Korea Contents Association
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• v.14 no.7
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• pp.83-92
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• 2014

We examined the annual film distribution portfolio of a Korean film distributor, Lotte Entertainment, investigating how the company puts big-budget films and low-budget movies together. As a result, the distributor was found to invest more than 80% of its whole budget in producing medium-size movies. The more successful box office, however, was witnessed from big-budget films that the company spent more than 6 billion Korean wons. With respect to genre, comedy and drama were the most and second-most frequently produced. However, Those two genres were not the most successful genres in the box office. Korean movie-goers favored actions and thriller the most and second most. Comedy took only the third place of the Korean box office, signifying a discord between the portfolio and the Korean box office.

• Journal of Technology Innovation
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• v.27 no.3
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• pp.27-52
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• 2019

This research studied the determinants of private R&D investment by examining the innovation strategies of 481 small and medium enterprises (SMEs, their employee size is 5 or more and less than 300) in Busan, South Korea. The data is derived from the Technology Survey of Small and Medium Enterprises in 2001 and 2003. Three explanatory variables for the innovation strategies are the R&D portfolio, the organization (personnel) for R&D, and the strategic role of CEO for innovation. The technological levels of industries are controlled in the linear regression model. The dependent variable is the total private R&D investment of a firm in the given fiscal year. The empirical results indicate that the private R&D investment positively correlates with the complexity of the R&D portfolio, the formal organization for R&D team, and the increase of R&D personnel. The formal organization for R&D team and the number of R&D personnel are correlated with the increase of private R&D investment across the four groups in the manufacturing sector but not in the service sector. These findings suggest that the innovation policy needs to target firms who have complex R&D portfolios, the formal organization of R&D teams, and sufficient R&D personnel in order to increase the private R&D investment of SMEs in regions, with consideration of industrial characteristics.