• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.05a
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• pp.161-174
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• 2017

최근 들어 기술벤처기업에 대한 투자가 증가하고, 이를 위한 기술신용평가의 역할이 증대하였다. 그러나 금융권에서 바라보는 기술신용평가의 경우, 해당 기업의 신용등급이나 기술(력) 등급평가에 초점을 두어, 대상기술의 사업화 및 수익성 관점을 체계적으로 반영하지 못하는 한계를 지닌다. 따라서, 벤처캐피털(VC)이나 엔젤투자자를 비롯한 금융권에서 대상기술의 수익성 정보를 참조하거나 기술벤처기업 설립시 기술지분을 참고하는 등, 기존 기술이전거래 협상참조용이나 담보 보증용에 널리 이용되던 기술가치평가의 활용범위가 급격히 확대되고 있다. 제조 서비스 분야의 일반 기술 뿐만이 아니라, 바이오 제약 의료 분야 기술에서도 미래 투입되어야 하는 사업화 소요기간 및 비용을 고려하여 기술가치를 산정해야 할 때가 있다. 기존의 현금흐름할인법(DCF법)이 연속된 투자에 대한 고려를 못하거나 기술적용 제품의 상용화 투입비용에 대한 확률적인 속성을 반영하지 못하는 등 한계점을 지니고 있다. 그러나 기술과 투자의 가치는 기회가치로 보고 자원배분을 위한 의사결정 정보를 감안해야 하므로, 실물옵션의 개념을 적용하는 것이 바람직하다고 여겨진다. 흔히 기업가치를 평가할 때 주가의 변동성(volatility) 개념을 도입하여 전일종가 대비 익일시가의 분산값을 활용하기도 한다. 이러한 개념을 기술가치평가에 적용하기 위해서는 '주가의 연속성(상대적 미세한 변화)' 및 '양(+)의 조건'을 고려해야 하는데, 실제 기술가치평가 상의 현금흐름은 사업초기년도 음(-)의 값이 나타나거나 2~3년 내외의 짧은 수익예상기간 하에서는 주가와 같은 변동성을 도출하는데 무리가 있다. 따라서 많은 문헌에서 연구된 바와 같이, 실물옵션 기반의 기술가치 산정을 위한 블랙-숄즈 모형에서 변동성과 기초자산가치, 그리고 사업화비용 간의 관계를 살펴볼 필요가 있다. 아울러 옵션가격결정모형(Option Pricing Model)에서 불확실성을 반영한 기초자산의 현재가치와 사업화비용의 현재가치분이 특정 임계조건 하에서 '옵션행사 포기(NAT; no action taken)' 영역으로 구분되는 지를 수학적으로 도출하고 관찰변수(입력값)에 따른 옵션가치 산출표를 개발하여 제시한다.

• The Korean Journal of Financial Management
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• v.12 no.2
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• pp.43-71
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• 1995

본(本) 연구(硏究)는 한국증권시장(韓國證券市場)에서 무위험자산이 존재하지 않을 경우에 대용시장(代用市場)포트폴리오의 효율성(效率性) 검증(檢證)을 다변량(多變量)의 검증방법(檢證方法)에 의해 실시하고자 하였다. 검증을 위하여는 첫째로 제로베타수익률을 구하고자 하였으며 이는 Kandel(1984)과 Shanken(1985)의 연구에 기초하여 추정하는 방법을 제시하였다. 둘째로 Jobson-Korkie(1982,1989), Handel(1984,1986)과 Shanken(1985,1986)의 논문(論文)에 의거 대용시장포트폴리오의 효율성을 검증하는 과정을 유도하고 이의 검증(檢證) 통계량(統計量)을 유도하였다. 효율성 검증에 사용된 대용시장포트폴리오들은 한국종합주가지수(韓國綜合株價指數), 한경(韓經)다우지수(指數)와 2개의 동일가중지수(同一加重指數)이었다. 실증적 연구결과, 전기간(全期間)의 경우에 한국종합주가지수(韓國綜合株價指數)와 한경(韓經)다우지수(指數)의 평균수익률은 GMVP의 수익률보다 낮을 뿐만아니라 평균-분산(平均-分散)프론티어의 외부(外部)에 위치하여 이 대용시장지수(代用市場指數)들이 효율적(效率的)프론터어상에 있는가에 대한 검증(檢證)은 불가능하였다. 그리고 하위기간별분석(下位期間別分析)에서도 이 대용시장지수(代用市場指數)(후반기(後半期)의 한경(韓經)다우지수(指數) 제외)들은 평균-분산프론티어의 내부(內部)에 있어 효율성 검증은 가능하였다. 그러나 이는 대용시장지수(代用市場指數)가 효율적 프론티어상에 있는가에 대한 효율성 검증이 아니라 단순히 평균-분산프론티어상에 있는가에 대한 검증에 불과하였으며 이러한 검증 결과는 효율적(效率的)인 것으로 나타났다. 한편 한국증권거래소(韓國證券去來所)에 상장(上場)된 전 종목(種目)을 대상으로한 동일가중지수(同一加重指數)와 본 연구의 기준시점인 1980년도에 한국증권거래소에 상장된 종목을 대상으로 구성한 동일가중지수(同一加重指數)는 이들의 평균수익(平均收益)이 GMVP의 수익률보다 높을 뿐만 아니라 대용시장지수(代用市場指數)가 효율적(效率的) 프론티어 상에 있는가에 대한 효율성 검증에서 모두효율적(效率的)인 것으로 나타났다. 이러한 사실은 한국종합주가지수(韓國綜合株價指數)를 근거로 기관투자자들이 투자수익율(投資收益率)의 관점에서 포트폴리오를 구성하거나 CAPM의 실증적(實證的) 연구시에 대용시장지수(代用市場指數)로 사용하는 것은 문제점이 있는 것으로 판단되며 따라서 동일가중지수(同一加重指數)를 선택함이 바람직할 것으로 사료된다.

• The Journal of Society for e-Business Studies
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• v.24 no.2
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• pp.125-141
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• 2019

This paper provides economic analysis for a residential photovoltaic (PV) power system of 5 districts in China and Thailand, using SAM (System Advisor Model) data. Unlike existing literature, the analysis is conducted from the investment timing perspective, as applying to a real option model which can incorporate the cost uncertainty of the PV system and a resident's option to delay the investment. This study shows that the gap of optimal investment times between a real option model and a generally used net present value model ranges from about 6 to 14 years. Also, we found a contracting result for a particular district that, while the investment is appropriate according to the net present value model, it is more reasonable to delay the PV system investment in terms of the real option model.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.199-207
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• 2019

This study aims to analyze the profitability of Robo-Advisors portfolio combined with the analysts' forecasts on the Korean stock prices. Sample stocks are 8 blue-chips and sample period is from 2003 to 2019. Robo-Advisor portfolio was suggested using the Black-Litterman model combined with the analysts' forecasts and its profitability was analyzed. Empirical result showed the suggested Robo-Advisor algorithm produced 1% annual excess return more than that of the benchmark. The study documented that the analysts' forecasts had an economic value when applied in the Robo-Advisor portfolio despite the prevalent blames from investors. The profitability on small or medium-sized stocks will need to be analyzed in the Robo-Advisor context because their information is relatively less known to investors and as such is expected to be strongly influenced by the analysts' forecasts.

• The Korean Journal of Financial Management
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• v.13 no.1
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• pp.1-49
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• 1996

금융정책(金融政策)과 자본시장(資本市場)의 증권가격(證券價格)과의 동태적(動態的) 관련성(關聯性)을 실증적으로 분석한 결과, 동태적(動態的) 구조면(構造面)에서 볼 때 특히 주식시장은 화폐에 대하여 시차를 갖는 반면, 장기채시장과 단기무위험시장에서는 시차를 갖지 않음이 발견되었다. 그리고 증권가격형성 메카니즘을 살펴본 결과, 화폐(貨幣)의 증가(增加)는 부(富)의 효과(效果)로 이어지고 그 결과 주가의 상승이 초래된다. 다른 한편으로 화폐의 증가가 대체효과를 반드시 유발시키지는 않으며, 그 이유로는 투자자들이 장기채를 선호함으로써 가격의 상승을 불러일으키는 반면 무위험자산의 경우 가격의 변동은 일어나지 않았기 때문이다. 끝으로, 가격효과(價格效果)의 존속은 비교적 단기로 끝남이 확인되었다. 증권시장(證券市場)의 장기적(長期的) 구조(構造)를 파악하기 위하여 공적분(共積分) 검정(檢定)을 실시하였다. 단위근(單位根) 검정(檢定)에 의하여 우리나라의 금융시계열은 단위근이 존재한다는 사실이 입증되었다. 따라서 금융시계열이 비정상적(非定常的) 확률과정(確率過程)을 따르고 있다. 공적분(共積分) 검정(檢定)에 의하여 화폐의 실질대수 유통속도와 실질주가, 장단기수익비율, 화폐차등수익률과 소득변화율 사이에는 차분후에 장기적 정상균형관계가 형성되고 있음이 발견되었다. 통화정책과 주가는 장기의 동태적 구조 측면에서 파악할 때 장기정상관계를 유지하고 있다. 그리고 주가는 시차(時差)가 0인 동시적(同時的) 통화정책(通貨政策)과 제1계 시차통화정책과 소득에 의하여 결정되고 있다. 다른 금융시계열은 주가결정에 영향을 미치고 있지 못한 실정이다. 주가의 상승은 부(富)의 효과(效果)와 대체효과(代替效果)를 유발하고 있다. 따라서 통화당국은 이점을 고려하여 화폐정책(貨幣政策)을 수립하여야 한다. 그리고 통화량은 주가의 가격형성에 양(陽)의 효과(效果)를 형성하고 있다. 따라서 화폐의 공급량의 증가는 명목주가(名目株價)의 상승을 유발하고 이에 따라 부(富)의 효과(效果)와 대체효과(代替效果)가 발생한다. 이와 같은 현상의 발견은 정부당국의 통화정책과 자본시장정책에 중요한 의의를 갖고 있다고 하겠다. 주가형성(株價形成)에 대한 통화정책의 효과는 당기 뿐만 아니라 차기에도 이어지고 화폐량과 소득이 주가의 결정에 영향을 미치고 있으며 다른 금융변수(金融變數)들은 영향을 미치지 않고 있다. 그러나 실질화폐잔고와 실질주가 장단기수익비율 화폐차등수익률과 소득변화률과는 장기적(長期的) 정상적(定常的) 균형관계(均衡關係)를 형성하고 있다. 따라서 장기적 관점에서 증권시장은 경제성장을 위한 통화정책과 각 분야의 균형적 성장을 유발할 수 있는 재정정책(財政政策)이 요청되고 있다. 위의 논의에서 유추할 수 있는 것은 화폐의 영향을 완화시키기 위하여 option시장의 개발과 농산물, 광물, 기타 실물 및 금융에 대한 선물시장의 개설이 요청된다. 이와 같은 시장을 통하여 통화 정책이 증권시장에 미치는 과도한 효과를 축소시켜 합리적이고 건전한 증권시장(證券市場)의 발전(發展)과 금융시장(金融市場)의 원활한 발전이 이룩될 수 있을 것이다. 자본시장이론(資本市場理論)에서는 화폐는 무시하고 실물적인 관점에서 증권가격의 결정을 연구하거나 위험분석에 주안점이 주어져 왔었다. 본 연구를 통하여 통화정책의 결과가 자본시장에 직접적으로 영향을 미치고 있음을 확인하였다. 통화금융정책과 주가의 유기적 관계를 확인한 본 논문의 결과를 정책당국이 참고하여 통화금융정책(通貨金融政策) 효율성(效率性)을 극대화(極大化)할 수 있을 것으로 본다.

• 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.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.139-160
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• 2017

Technology commercialization creates effective economic value by linking the company's R & D processes and outputs to the market. This technology commercialization is important in that a company can retain and maintain a sustained competitive advantage. In order for a specific technology to be commercialized, it goes through the stage of technical planning, technology research and development, and commercialization. This process involves a lot of time and money. Therefore, the duration and cost of technology commercialization are important decision information for determining the market entry strategy. In addition, it is more important information for a technology investor to rationally evaluate the technology value. In this way, it is very important to scientifically estimate the duration and cost of the technology commercialization. However, research on technology commercialization is insufficient and related methodology are lacking. In this study, we propose an evaluation model that can estimate the duration and cost of R & D technology commercialization for small and medium-sized enterprises. To accomplish this, this study collected the public data of the National Science & Technology Information Service (NTIS) and the survey data provided by the Small and Medium Business Administration. Also this study will develop the estimation model of commercialization duration and cost of R&D performance on using these data based on the market approach, one of the technology valuation methods. Specifically, this study defined the process of commercialization as consisting of development planning, development progress, and commercialization. We collected the data from the NTIS database and the survey of SMEs technical statistics of the Small and Medium Business Administration. We derived the key variables such as stage-wise R&D costs and duration, the factors of the technology itself, the factors of the technology development, and the environmental factors. At first, given data, we estimates the costs and duration in each technology readiness level (basic research, applied research, development research, prototype production, commercialization), for each industry classification. Then, we developed and verified the research model of each industry classification. The results of this study can be summarized as follows. Firstly, it is reflected in the technology valuation model and can be used to estimate the objective economic value of technology. The duration and the cost from the technology development stage to the commercialization stage is a critical factor that has a great influence on the amount of money to discount the future sales from the technology. The results of this study can contribute to more reliable technology valuation because it estimates the commercialization duration and cost scientifically based on past data. Secondly, we have verified models of various fields such as statistical model and data mining model. The statistical model helps us to find the important factors to estimate the duration and cost of technology Commercialization, and the data mining model gives us the rules or algorithms to be applied to an advanced technology valuation system. Finally, this study reaffirms the importance of commercialization costs and durations, which has not been actively studied in previous studies. The results confirm the significant factors to affect the commercialization costs and duration, furthermore the factors are different depending on industry classification. Practically, the results of this study can be reflected in the technology valuation system, which can be provided by national research institutes and R & D staff to provide sophisticated technology valuation. The relevant logic or algorithm of the research result can be implemented independently so that it can be directly reflected in the system, so researchers can use it practically immediately. In conclusion, the results of this study can be a great contribution not only to the theoretical contributions but also to the practical ones.

• Knowledge Management Research
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• v.21 no.3
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• pp.45-65
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• 2020

It is desirable to invest in wine that increases its value, but wine investment itself is unfamiliar in Korea. Also, the process itself is unreasonable, and information is often forged, because pricing in the wine market is done by a small number of people. With the right solution, however, the wine market can be a desirable investment destination in that the longer one invests, the higher one can expect. Also, it is expected that the domestic wine consumption market will expand through the steady increase in domestic wine imports. This study presents the consortium block chain framework for revitalizing the wine market and enhancing transparency as the "right solution" of the nation's wine investment market. Blockchain governance can compensate for the shortcomings of the wine market because it guarantees desirable decision-making rights and accountability. Because the data stored in the block chain can be checked by consumers, it reduces the likelihood of counterfeit wine appearing and complements the process of unreasonably priced. In addition, digitization of assets resolves low cash liquidity and saves money and time throughout the supply chain through smart contracts, lowering entry barriers to wine investment. In particular, if the governance of the block chain is composed of 'chateau-distributor-investor' through consortium blockchains, it can create a desirable wine market. The production process is stored in the block chain to secure production costs, set a reasonable launch price, and efficiently operate the distribution system by storing the distribution process in the block chain, and forecast the amount of orders for futures trading. Finally, investors make rational decisions by viewing all of these data. The study presented a new perspective on alternative investment in that ownership can be treated like a share. We also look forward to the simplification of food import procedures and the formation of trust within the wine industry by presenting a framework for wine-owned sales. In future studies, we would like to expand the framework to study the areas to be applied.

• The Korean Journal of Financial Management
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• v.21 no.2
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• pp.27-63
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• 2004

Since the 1980s, many multinational corporations have been issuing stocks on foreign stock exchanges, not only to enhance their investor base and liquidity, but also to diversify risks. The phenomenon has also been intensified by the rapid financial globalization and securitization trends. The main purpose of this study is to look into the long-run performance of MNCs' cross-listings of stocks on foreign stock exchanges. We use the event study and cross-sectional regression methods. We obtained some interesting empirical results about the long-run effect of cross-listings. First before the listing data the effect of cross-listing is to increase the underlying stock Vice in the local market. It may be caused by expectation of lower risk and cost of capital. However, after the listing data the stock price has been declining, even if it is not significant. Second, we examine the difference in the long-run cross-listing effect, which may be caused by the listing direction. When listing is made from a less developed market to a more developed market, the effect is better than that in the reverse direction. Furthermore, the effect is worse, when the listing company's home country is the U.S. Third, there is a negative relation between CARs and underlying stock liquidity in the local market, So it implies that a firm, whose underlying stocks are very liquid in the local market should carefully value cross-listing based upon the cost and benefit analysis. Last, but not the least we find that the long-un cross-listing effect is better, when a listing firm's ROE is higher.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.1
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• pp.1-19
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• 2021

The purpose of this research is to improve the investment success rate for startups in the water industry for the development of the entrepreneurial environment of the Korean water industry. In this research, we identified investment determinants through prior research and stratified them, and then surveyed the investor group at the beginning of the start-up using the FGI method, and determined the order of the investment determinants of investors. At the same time, we classified 41 start-ups related to the water industry into two groups: the group that received investment and the group that did not in the early stages of the start-up. Then we investigated the understanding of the investor's investment determinants, ranked them, and compared them by using the AHP technique. Through this, this research proposes five implications. First, it is important for start-ups in the early stages to receive seed investment to revitalize investment for startups in the water industry. For this, startups need to understand investors and prepare to attract investment with the perspective of angel investors rather than the perspective of VC investors. Second, Start-ups in the water sector should consider that the characteristics of the founder are important in order to receive seed investment, and also need to define their business at the industry and market level, and provide relevant rationale to meet the expectations of investors who value industry expertise and experience, and to increase the possibility of seed investment, which is important in the early stages of a startup. Third, institutions, such as K-water(Korea Water Resources Corporation), that support water industry startups need to conduct open innovation business opportunities discovery programs linked to startups so that startups currently participating in the startup support program could have business opportunities from the business infrastructure of platform-forming companies in the water industry. In particular, such institutions should help founders develop their industrial expertise and careers by supporting this type of start-up preparation process through the participation of in-house venture founders. Fourth, when K-water uses the government start-up support fund to discover and foster founders, it should increase initial contact with seed investors, conduct more thorough verification of business plans, and develop programs that use government start-up support funds to prepare a business suitable for seed angel investors. Fifth, K-water should support seed by connecting funds for initial investment among funds operated by itself. It is also necessary to develop a program that links the company receiving the seed investment with VC investment, not angel investment in cooperation with the VC fund operation entity participating as an LP so that companies that have attracted seed investment could attract follow-up VC investment.