• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.266-267
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• 2015

The information security (information security knowledge) industry is technology-intensive, high value-added industries. South Korea's response has excellent ICT skills and experience and skills in a variety of cyber attacks, has become a benchmark in the world. However, the small size of the domestic information security company, supporting infrastructure is lacking. Domestic information security industry is the primary condition to activate the export. In order to export it is necessary to develop for the Korean Cybersecurity Goods Menu for products, services and technologies of the domestic information security industry. Buyer Menu as well as foreign and domestic Seller, companies and governments know the details of the assets and product information security industry and is a prerequisite to sell. In this paper, I study the Korean Cybersecurity Goods Menu development.

• 정보화사회
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• s.184
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• pp.28-39
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• 2007

IT산업은 항상 끊임없는 '혁신'을 통해서만 성장의 자양분을 얻는다. 기술적 또는 개념적 진보가 없다면 IT산업은 당연히 산업으로서의 존재가치가 없다. 그렇다면 2007년, 한국의 IT시장을 주도할 '10대 핵심 IT기술'들은 어떤 것들일까. 국내 통신 및 네트워크, 솔루션 업계가 가장 관심 있게 지켜보는 IT기술등 중 10개를 선별했다. 10대 IT기술에 포함된 것들 중에는 개념적으로는 10년전부터 출현해 알려져 있지만, 그동안 단순히 개념에 머물렀을 뿐 아직 상용화가 안 된 것들도 일부 포함돼 있다. 참신성보다는 실질적인 의미와 중요성에 무게들 둔 때문이다. 2007년에 주목할 만한 10대 IT기술로는 먼저 'IP 멀티미디어 서브시스템(IMS)이 꼽힌다. 유무선을 비롯한 다양한 네트워크를 자연스럽게 연결해주고 여러 서비스들의 다양한 조합이 가능해 고객맞춤형서비스를 가능하게 하는 '멀티미디어 멀티캐스트 컨트롤(MMC)도 10대 IT기술에 포함됐다. 이와 함께 100Mbps 초고속 인터넷 서비스를 가능하게 하는 ETTH (Ethernet To The Home), 차세대 보안이슈인 네트워크접근제어(NAC), 금융회사나 이동통신회사의 상품개발을 신속하게 지원하는 '프로덕트 팩토리', 하드웨어시스템의 최적화를 보장한 가상화 및 ILM, 컴퓨팅산업을 한 단계 이상 진화시킬 쿼드코어 CPU, 소프트웨어 시장의 새로운 패러다임인 SOA(서비스지향아키텍처), 본격적인 시장 확산이 기대되는 전자태그(RFID), IT거버넌스의 열기로 급부상하고 있는 ITAM(IT자산관리) 등도 올해 주목할 10대 IT기술 반열에 올랐다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.797-800
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• 2021

인공지능을 활용한 주가 예측 모형을 실제 금융 서비스에 도입한 사례가 많아지고 있다. 주식 데이터는 일반적인 시계열 데이터와 다르게 예측을 어렵게 하는 복합적인 요소가 존재하며 주식은 리스크가 큰 자산 상품 중 하나이다. 주가 예측 모형의 활용 가능성을 높이기 위해선 성능을 향상시키는 것과 함께 모델을 해석 가능한 형태로 제시해 신뢰성을 향상시킬 필요성이 있다. 본 논문은 주가 전망 결정 방법에 따른 예측 결과를 비교하고, 설명 가능성을 부여해 모형 개선했다는 것에 의의가 있다. 연구 결과, 주가 전망을 장기적으로 결정할수록 정확도가 증가하고, XAI 기법을 통해 모형의 개선 근거를 제시할 수 있음을 알 수 있었다. 본 연구를 통해 인공지능 모형의 신뢰성을 확보하고, 합리적인 투자 결정에 도움을 줄 수 있을 것으로 기대한다.

• 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 Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Cartoon and Animation Studies
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• s.28
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• pp.155-180
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• 2012

This article is to propose a conceptual framework for the One Source Multi Use (OSMU) strategic model of culture content. In this study, OSMU is defined as a series of marketing activities to increase the value created by culture content. The framework of brand equity strategy is applied to develop the strategic model of OSMU, as both share the same goal - maximization of long term value created by brand or culture content. This article suggests the brand identity-based OSMU strategic model, in which the brand identity of culture content guides, integrates, and coordinates every decision-making of OSMU activities. For the maximization of brand equity value of culture content, the copyright holder of original content should decide the content's brand identity, which should provide the criteria of all decision makings regarding window strategy, adaptation of content to other genre, and merchandising. This brand identity-based OSMU strategic model can not only contribute to the sales of OSMU content, but also the creation of high equity culture content in the long run. Also, the model allows monitoring and evaluation of content's brand equity, which can be used for the strategic adjustment for the sake of long term value of the content. This study is differentiated from previous study on OSMU and expected to invigorate the further discussion on OSMU in several points. First, it broadens the scope of OSMU discussion as it views OSMU as a series of process including feedback. Second, this study points out the need for integration and coordination of various OSMU activities. Third, the strategic focus is laid on the value maximization of the original content, not 'multi used' content. Fourth, the suggested model emphasize the strategic role of copyright holder who takes the charge of the content brand management. Fifth, the model requires discussion on the components of marketing communication in addition to the content itself, which means the model includes the prospects, not only the content consumers, as the major future source for value creation.

• Archives of design research
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• v.19 no.1 s.63
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• pp.141-150
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• 2006

The animation should be produced to attract the audience's interests. Its characters will have the intended personalities through the interactions with the audiences, discarding the attributes of the nature. These personalities form the brand identity when they are exposed visually and will be powerful brand assets which lead the animation industry to the high-value added products. The brand identity of the character, the brand assets, can be used for various products in form of licensing and is noted to make an affirmative leverage effect. In this regard, the author has conducted an empirical research on the animation characters from the viewpoint of the brand, adopting, in particular, the Brand Personality Scale (BPS), which is the output of J. Aaker's (1997) study on brand personality defined as the human properties in relation with the brand. In addition, this study determines the correlation among the animation, brand and consumers based on the Sirgy's study (1982) resulting in that the better the brand and the consumer's personality are matched, the more the brand attitude is improved. In consequence, it is found that the animation characters have three personality levels such as refinement/ability, integrity and interests. The consumer's personality is divided into the 'practical ego-image' and the 'ideal ego-image' in the survey, and the survey result shows that the brand personality of the animation character exists between them.

• KDI Journal of Economic Policy
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• v.14 no.2
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• pp.189-203
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• 1992

Any money model should address the most important phenomenon of a monetary economy, which is the phenomenon of the rate of return dominance. Even if the holding returns on financial or nonfinancial assets are higher than the rate of return on fiat money holding, which is typically zero, people still hold and use money. In a period of accelerating inflation, number of dominating assets increases continuously, yet people continue to hold and use money. Wallace's (1980) overlapping generations model cannot address the rate of return dominance phenomenon. His model does not capture the mediun of exchange role of fiat money. In this paper, an overlapping types model of fiat money is constructed, in which different types of consumers have different preferences on different types of goods, are endowed with different types of goods, are located at seperated regions, and live for only two periods. In this model, people hold and use money despite the dominating assets, even if inflation accelates. Money in this case serves as a pure medium of exchange, whereas in Wallace's model, money serves as a pure store of value, and money disappears if a dominating asset exists. An interesting feature of the overlapping types model presented in this paper is that money does not provide a cheap approximation to an idealized and efficient real allocation. A monetary economy is always superior to a nonmonetary economy, because money helps overcome the incompleteness of the overlapping types friction. In a monetary economy, however, a pareto optimal allocation cannot always be achieved, because money cannot always overcome the overlapping types friction itself. Therefore, with the criterion of optimality of real allocations, the monetary economy is more optimal than a nonmonetary economy but less optimal than a complete Arrow-Debreu economy. This feature has important implications on macro modelling. Because of the difficulty in introducing money into a macro model in an essential and endogenous manner as in the overlapping types model of this paper, a macro model typically ignores money and studies real allocations without the money factor. The possible inefficiencies of a monetary economy, relative to a complete real Arrow-Debreu economy, may indicate differences in real allocations between the two models.

• Journal of the Economic Geographical Society of Korea
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• v.12 no.4
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• pp.665-680
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• 2009

This study aims at analyzing the performance of Real Estate Investment Trusts(REITs) by Risk Adjustment Model. The main results are as follows. Firstly, most property types of REITs gain positive(+) excess overall returns at first and second period. On the contrary, most property types of REITs gain negative(-) excess overall returns and their standard deviations are larger at financial crisis period. Secondly, lodging, regional mall and commercial mortgage show lower risk-lower return, and freestanding, apartment and specialty show higher risk-higher return than average REITs, according to the CAPM results of . Moreover CAPM results of show the characteristics of REITs as investment commodities changes into higher risk-higher return for financial crisis period. Lastly, risk adjusted demanded returns of REITs are affected positively(+) by systemic risks and negatively(-) by unsystemic risks, according to the Risk Adjustment Model results of both and . Comparing risk adjusted demanded returns of REITs with their realized returns, healthcare reveals the largest outperformance.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.1
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• pp.17-28
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• 2019

In the financial investment management strategy, the distributed investment selecting and combining various financial assets is called portfolio management theory. In recent years, the blockchain based financial assets, such as cryptocurrencies, have been traded on several well-known exchanges, and an efficient portfolio management approach is required in order for investors to steadily raise their return on investment in cryptocurrencies. On the other hand, deep learning has shown remarkable results in various fields, and research on application of deep reinforcement learning algorithm to portfolio management has begun. In this paper, we propose an efficient financial portfolio investment management method based on Asynchronous Advantage Actor-Critic (A3C), which is a representative asynchronous reinforcement learning algorithm. In addition, since the conventional cross-entropy function can not be applied to portfolio management, we propose a proper method where the existing cross-entropy is modified to fit the portfolio investment method. Finally, we compare the proposed A3C model with the existing reinforcement learning based cryptography portfolio investment algorithm, and prove that the performance of the proposed A3C model is better than the existing one.