• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.436-438
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• 2017

자본주의의 꽃인 주식시장은 파생시장에 의해 영향을 받고 있으며, 파생시장은 지수옵션 상품에 의해 영향을 받고 있다. 최근 들어 시스템 트레이딩에 대한 관심이 점점 더해가고 있으며 투자자에게 컴퓨터 시스템과 매매 전략에 대한 이해를 요구하고 있다. 지수옵션 시장은 만기일을 기준으로 마치 파도와 같이 순간순간 살아 움직이고 있다. 옵션에 대한 효과적인 관점은 투자자에게 확률 높은 매력적인 전략을 제공하며 옵션의 움직임을 전체적으로 해석할 수 있게 한다, 그리고 궁극적으로 옵션가의 예측을 가능하게 한다. 행사가와 방향성에 의한 개별 옵션은 함수로 해석될 수 있다. 다양한 입력값에 의해 가격이라는 하나의 출력값이 결정되는 구조이다. 입력값에는 지수, 시간, 거래량 의 세가지 카테고리로 이루어진다. 이중 거래량은 예측이 가능한데, 개별 옵션이 아닌 앙상불의 경우 출력값으로 처리될 수 있다. 하지만 앙상불 옵션에서 개별 옵션가는 경직성을 가지게 되어 예상가의 차이에 의한 압력이 발생하게 된다. 이 압력은 이후의 지수변화에 핵심적인 에너지로 작용할 수 있다. 압력의 측정은 다양한 방법이 있을 수 있는데, 본 논문에서는 뉴로-퍼지 시스템을 이용한 예측값과의 차이를 측정하여 계산하였다. 일단 학습된 뉴로-퍼지 시스템은 가격을 예측하게 되며, 실제 가격과의 괴리는 압력으로 해석할 수 있다.

• 한국벤처창업학회:학술대회논문집
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• 2022.11a
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• pp.167-173
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• 2022

임팩트투자는 재무적인 수익과 더불어 사회에 긍정적인 영향을 만들고자 하는 의도를 가진 기업, 조직, 펀드를 대상으로 하는 투자 방식으로서 사회적 문제를 해결하기 위해 혁신적인 기술과 아이디어를 기잔 소셜벤처 기업에 재무적 지원을 위한 초기단계 투자로서 많은 관심을 받고 있다. 임팩트투자자는 재무적 수익과 사회적 가치추구라는 이중적인 판단 요인 때문에 사회적가치지표(SVI: Social Value Index)를 투자판단에 참고로 활용하고 있으나 투자를 결심하는 판단요인에 있어서는 여전히 개인의 경험과 주관적인 판단에 의존하고 있는 실정이다. 본 연구를 통하여 임팩트투자자가 복합적으로 고려하는 투자판단 요인들에 대한 타당도와 상대적 가중치를 객관화 하고, 재무적 수익과 사회적 가치의 이중적이 가치판단 중에서 어느 쪽을 더 주요하게 고민하는 지에 대하여 분석하였다. 본 연구는 판단분석기법을 활용하였으며 '임팩트투자자의 소셜벤처 투자결정에 대한 종합적인 평가'를 판단문제로 정의하고, 임팩트투자자의 투자의사결정 요인을 도출하기 위하여 투자자로서 재무적 이익과 회수 가능성 판단을 위한 ①초기투자단계에서의 투자의사결정 요인, 사회에 미치는 영향과 파급력, 소셜벤처의 상생과 연대를 위한 ②창업가(팀)의 정치적 기술, 임팩트투자펀드 조성 목적에 부합하는 ③소셜벤처기업의 소셜미션 등 세가지의 분류로 구성하여 연구를 진행하였다.

• The Magazine for Energy Service Companies
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• s.73
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• pp.52-55
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• 2011

'2011 대한민국 녹색에너지대전'이 지식경제부 주최, 에너지관리공단 주관으로 지난 10월 19일 서울 삼성동 코엑스(A,B홀)에서 개최되었다. 올 해로 31회를 맞이한 대한민국 녹색에너지대전은 '세이브 에너지, 스마트 라이프(Save Energy, Smart Life)라는 주제로 13개국, 247개 업체가 참가하였으며, 에너지절약 및 효율 향상 분야, 신재생에너지분야, 기후변화대응분야 등 녹색에너지 분야 전반을 총망라한 최신 녹색기술 제품들이 전시되었다. 이번 전시회는 코트라 주관의 수출상담회, 신재생에너지 유공자 포상이 열렸으며, 일반인 대상의 '신재생에너지 체험관'을 전시기간 내내 운영해 일반 관람객들이 태양광 선풍기 풍력자동차 등을 직접 조립해 보도록 해 관람객들의 호응을 얻었다. 21일 개최된 ESCO투자 사례 발표회에서는 산업체 건물에서 추진된 ESCO 투자 사례 정보를 제공해 주는 뜻 깊은 시간이 되었다. 특히 이번 전시회에는 대내외적으로 에너지절약에 대한 뜨거운 관심이 고조되는 가운데 롯데, LG전자, 삼성전자를 비롯해 효성, 삼천리 등 대기업이 대거 참가, 에너지효율 및 온실가스 저감 최신 기술을 선보였다. 이번 전시회에서는 에너지효율과 신재생에너지에 관심이 많은 관람객들과 최신에너지 기술을 도입하고자 하는 산업체 임직원, 신재생에너지 및 기후 변화대응분야 신규 사업에 관심이 있는 투자자 등 전시장을 찾는 모두에게 유익한 정보를 제공하는 뜻깊은 시간이 되었다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.216-221
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• 2011

After the year of 2000, the Korean government has abolished the limit on foreign investments. Foreign investments in the domestic market have been thriving since. In domestic stock market, the proportion of market value held by foreign investors reaches over 40%. There are many followers in the markets, asking about what kinds of the firm that foreign investors prefer. Prior researches show that foreign investors in the American and European markets prefer stocks of the firm which are well known and are geographically closer. In this paper, we attempt to define the financial characteristics of the firms in which foreigners invest in the Korean market. The result shows that foreign investors in the domestic market tend to prefer firms with high market value of capital and dividend yield. It also shows that foreign investors in the Korean market choose firms with high book value to market value over others, while the firms with high debt ratio and the portion of the largest stock holders are shunned. This research suggests that foreign portfolio investments in the Korean market have contributed to liquidity of stock market and changed the governance structure of domestic firms in a positive way.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.147-157
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• 2023

This paper investigates the financial factors affecting angel investors' investment decisions for 818 firms from 2009 to 2018 in the Korean venture investment market. We construct a quasi-experimental design using propensity scoring matching and compare the investment determinants between investment firms and matching firms. The main empirical findings are as follows. First, we find that angel investors are more likely to choose firms based on a firm's growth such as profit and assets rather than profitability or financial stability. In addition, we identify that they prefer the firm not only higher intangible assets but also higher R&D expenditures. Second, we find that angel investors consider both growth and activity ratios in the firms for over three years and have entered the mid-stage of startups. Overall, we confirm that the investment decision of angel investors mainly focuses on the venture startups' growth trend or future growth potential rather than the realized profitability or financial stability. We also infer that the possibility of performance creation is an important investment factor along with growth for the mid-stage startup.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.1-11
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• 2024

In the midst of the current turbulent global economy, traditional investment metrics are undergoing a metamorphosis, signaling the onset of what's often referred to as an "Investment cold season". Early-stage startups, despite their boundless potential, grapple with immediate revenue constraints, intensifying their pursuit of critical investments. While financial indicators once took center stage in investment evaluations, a notable paradigm shift is underway. Organizational culture, once relegated to the sidelines, has now emerged as a linchpin in forecasting a startup's resilience and enduring trajectory. Our comprehensive research, integrating insights from CVF and OCAI, unveils the intricate relationship between organizational culture and its magnetic appeal to investors. The results indicate that startups with a pronounced external focus, expertly balanced with flexibility and stability, hold particular allure for investment consideration. Furthermore, the study underscores the pivotal role of adhocracy and market-driven mindsets in shaping investment desirability. A significant observation emerges from the study: startups, whether they secured investment or failed to do so, consistently display strong clan culture, highlighting the widespread importance of nurturing a positive employee environment. Leadership deeply anchored in market culture, combined with an unwavering commitment to innovation and harmonious organizational practices, emerges as a potent recipe for attracting investor attention. Our model, with an impressive 88.3% predictive accuracy, serves as a guiding light for startups and astute investors, illuminating the intricate interplay of culture and investment success in today's economic landscape.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.17-32
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• 2023

In recent years, the need for social ventures that aim to grow while solving social problems through the efficiency and effectiveness of commercial organizations in the market has increased, while there is a limit to how much the government and the public can do to solve social problems. Against this background, the number of social venture startups is increasing in the domestic startup ecosystem, and interest in impact investors, which are investors in social ventures, is also increasing. Therefore, this research utilized judgment analysis technology to objectively analyze the validity and weight of judgment information based on the cognitive process and decision-making environment in the investment decision-making of impact investors. We proceeded with the research by constructing three classifications; first, investment priorities at the initial investment stage for financial benefit and return on investment as an investor, second, the political skills of the entrepreneurs (teams) for the social impact and ripple power, and social venture coexistence and solidarity, third, the social mission of a social venture that meets the purpose of an impact investment fund. As a result of this research, first of all, the investment decision-making priorities of impact investors are the expertise of the entrepreneur (team), the potential rate of return when the entrepreneur (team) succeeds, and the social mission of the entrepreneur (team). Second, impact investors do not have a uniform understanding of the investment decision-making factors, and the factors that determine investment decisions are different, and there are differences in the degree of the weighting. Third, among the various investment decision-making factors of impact investment, "entrepreneur's (team's) networking ability", "entrepreneur's (team's) social insight", "entrepreneur's (team's) interpersonal influence" was relatively lower than the other four factors. The practical contribution through this research is to help social ventures understand the investment determinant factors of impact investors in the process of financing, and impact investors can be expected to improve the quality of investment decision-making by referring to the judgment cases and analysis of impact investors. The academic contribution is that it empirically investigated the investment priorities and weighting differences of impact investors.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.321-324
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• 2000

최근 국내 경제의 구조적 전환에 대한 필요성이 증대됨에 따라 기술집약적인 산업에 관심이 모아지고 있으며 이러한 관심은 주로 기술개발과 혁신을 통해 고수익을 달성하려는 벤처기업에 집중되어져 왔다. 특히 인터넷 비즈니스분야의 벤처인 경우 기존 산업이 지니고 있던 산업구조, 기업유형 및 수익원천과는 구별되는 특징을 지니고 있기에 기업의 성장성과 수익성을 예측하고 미래가치를 판단하는데 있어 새로운 투자평가 패러다임이 요구된다. 국내의 경우 최근 들어 벤처성과와 관련된 연구들이 진행되고는 있으나 주로 사례연구 방식으로 벤처기업을 대상으로 한 주요성공요인, 성과 영향요인, 자금조달 및 육성지원정책 등 탐색적 연구에서 벗어나지 못하고 있으며 실제 벤처투자자의 입장에서의 연구는 이루어지지 못하고 있다. 따라서 본 연구는 벤처투자가를 대상으로 투자고려요인과 성과요인을 추출하고 두 요인간의 관련성을 파악함으로써 투자성과에 영향을 미치는 요인을 제시하고 이를 투자자유형, 성장단계, 투자시기, 사업유형, 벤처전략별로 분석하는 것을 목표로 하고 있다. 이를 통해 인터넷 비즈니스분야의 벤처기업에게는 각 기업별 특성에 따른 관리 요인과 벤처투자가에게는 투자의사 결정시 도움을 줄 수 있는 투자지침을 제시한다.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.