• Title/Summary/Keyword: artificial capital market

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Distributed artificial capital market based planning in 3D multi-robot transportation

  • Akbarimajd, Adel;Simzan, Ghader
    • Advances in robotics research
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    • v.1 no.2
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    • pp.171-183
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    • 2014
  • Distributed planning and decision making can be beneficial from the robustness, adaptability and fault tolerance in multi-robot systems. Distributed mechanisms have not been employed in three dimensional transportation systems namely aerial and underwater environments. This paper presents a distributed cooperation mechanism on multi robot transportation problem in three dimensional environments. The cooperation mechanism is based on artificial capital market, a newly introduced market based negotiation protocol. In the proposed mechanism contributing in transportation task is defined as asset. Each robot is considered as an investor who decides if he is going to invest on some assets. The decision is made based on environmental constraint including fuel limitation and distances those are modeled as capital and cost. Simulations show effectiveness of the algorithm in terms of robustness, speed and adaptability.

A Study on Global Blockchain Economy Ecosystem Classification and Intelligent Stock Portfolio Performance Analysis (글로벌 블록체인 경제 생태계 분류와 지능형 주식 포트폴리오 성과 분석)

  • Kim, Honggon;Ryu, Jongha;Shin, Woosik;Kim, Hee-Woong
    • 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.

Suggestions for the Development of RegTech Based Ontology and Deep Learning Technology to Interpret Capital Market Regulations (레그테크 기반의 자본시장 규제 해석 온톨로지 및 딥러닝 기술 개발을 위한 제언)

  • Choi, Seung Uk;Kwon, Oh Byung
    • The Journal of Information Systems
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    • v.30 no.1
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    • pp.65-84
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    • 2021
  • Purpose Based on the development of artificial intelligence and big data technologies, the RegTech has been emerged to reduce regulatory costs and to enable efficient supervision by regulatory bodies. The word RegTech is a combination of regulation and technology, which means using the technological methods to facilitate the implementation of regulations and to make efficient surveillance and supervision of regulations. The purpose of this study is to describe the recent adoption of RegTech and to provide basic examples of applying RegTech to capital market regulations. Design/methodology/approach English-based ontology and deep learning technologies are quite developed in practice, and it will not be difficult to expand it to European or Latin American languages that are grammatically similar to English. However, it is not easy to use it in most Asian languages such as Korean, which have different grammatical rules. In addition, in the early stages of adoption, companies, financial institutions and regulators will not be familiar with this machine-based reporting system. There is a need to establish an ecosystem which facilitates the adoption of RegTech by consulting and supporting the stakeholders. In this paper, we provide a simple example that shows a procedure of applying RegTech to recognize and interpret Korean language-based capital market regulations. Specifically, we present the process of converting sentences in regulations into a meta-language through the morpheme analyses. We next conduct deep learning analyses to determine whether a regulatory sentence exists in each regulatory paragraph. Findings This study illustrates the applicability of RegTech-based ontology and deep learning technologies in Korean-based capital market regulations.

A study on the prediction of korean NPL market return (한국 NPL시장 수익률 예측에 관한 연구)

  • Lee, Hyeon Su;Jeong, Seung Hwan;Oh, Kyong Joo
    • Journal of Intelligence and Information Systems
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    • v.25 no.2
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    • pp.123-139
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    • 2019
  • The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

Trends and Implications of Venture Capital Investment in the Artificial Intelligence Industry (인공지능(AI) 산업의 VC 투자 동향과 시사점)

  • S.S., Choi;B.R., Joo;S.J., Yeon
    • Electronics and Telecommunications Trends
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    • v.37 no.6
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    • pp.1-10
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    • 2022
  • Artificial intelligence (AI) has rapidly diffused across industries and societies as nations' essential strategic technology. In innovative technology, such as AI, a startup leads to technological innovation and significantly impacts the expansion of relevant industries. Thus, this study examined the trend of AI startup venture capital (VC) investments globally, focusing on ① noteworthy VC investment statuses (the number and size of the investment, company establishment, and corporate collection), ② the characteristics of each key nation's investments, and ③ the characteristics of each submarket's investments. Among the 11 countries, the results showed that Korea ranked near the bottom for absolute quantitative measures, including the number and size of investments, company establishment, and corporate collection. However, Korea has built a foundation of catching up with what AI-leading countries have established, considering Korea's high growth rate in the number and size of investments and a recent mega-round. This study has practical implications in that it determined the AI startup VC investment status of Korea's rival countries, not only G2 (US and China). The results can be used in policy-making. Furthermore, identifying the AI industry's submarkets and analyzing each market's VC investment status could be used to establish strategies for the AI industry and R&D.

Panamax Second-hand Vessel Valuation Model (파나막스 중고선가치 추정모델 연구)

  • Lim, Sang-Seop;Lee, Ki-Hwan;Yang, Huck-Jun;Yun, Hee-Sung
    • Journal of Navigation and Port Research
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    • v.43 no.1
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    • pp.72-78
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    • 2019
  • The second-hand ship market provides immediate access to the freight market for shipping investors. When introducing second-hand vessels, the precise estimate of the price is crucial to the decision-making process because it directly affects the burden of capital cost to investors in the future. Previous studies on the second-hand market have mainly focused on the market efficiency. The number of papers on the estimation of second-hand vessel values is very limited. This study proposes an artificial neural network model that has not been attempted in previous studies. Six factors, freight, new-building price, orderbook, scrap price, age and vessel size, that affect the second-hand ship price were identified through literature review. The employed data is 366 real trading records of Panamax second-hand vessels reported to Clarkson between January 2016 and December 2018. Statistical filtering was carried out through correlation analysis and stepwise regression analysis, and three parameters, which are freight, age and size, were selected. Ten-fold cross validation was used to estimate the hyper-parameters of the artificial neural network model. The result of this study confirmed that the performance of the artificial neural network model is better than that of simple stepwise regression analysis. The application of the statistical verification process and artificial neural network model differentiates this paper from others. In addition, it is expected that a scientific model that satisfies both statistical rationality and accuracy of the results will make a contribution to real-life practices.

Study on Predicting the Designation of Administrative Issue in the KOSDAQ Market Based on Machine Learning Based on Financial Data (머신러닝 기반 KOSDAQ 시장의 관리종목 지정 예측 연구: 재무적 데이터를 중심으로)

  • Yoon, Yanghyun;Kim, Taekyung;Kim, Suyeong
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.17 no.1
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    • pp.229-249
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    • 2022
  • This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.

The Impact of Social Capital and Laboratory Startup Team Diversity on Startup Performance Based on a Network Perspective: Focusing on the I-Corps Program (네트워크 관점에 기반한 사회적 자본 및 실험실 창업팀 다양성이창업 성과에 미치는 영향: I-Corps program을 중심으로)

  • Lee, Jai Ho;Sohn, Youngwoo;Han, Jung Wha;Lee, Sang-Myung
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.18 no.6
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    • pp.173-189
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    • 2023
  • As supreme technologies continue to be developed, industries such as artificial intelligence, biotechnology, robots, aerospace, electric vehicles, and solar energy are created, and the macro business environment is rapidly changing. Due to these large-scale changes and increased complexity, it is necessary to pay attention to the effect of social capital, which can create new value by utilizing capital increasing the importance of relationships rather than technology or asset ownership itself at the level of start-up strategy. Social capital is a concept first proposed by Hanifan in 1916, and refers to the overall sum of capabilities or resources that are latent or available for use in mutual, continuous, organic relationships or accumulated human relationship networks between individuals or social members. In addition, the diversity of start-up teams with diverse backgrounds, characteristics, and capabilities, rather than one exceptional founder, has been emphasized. Founding team diversity refers to the diversity of in-depth factors such as demographic factors, beliefs, and values of the founding team. In addition, changes in the macro environment are emphasizing the importance of technology start-ups and laboratory start-ups that lead industrial innovation and create the nation's core growth engines. This study focused on the I-Corps' program. I-Corps, which means innovation corps, is a laboratory startup program launched by the National Research Foundation (NSF) in 2011 to encourage entrepreneurship and commercialization of research results. It focuses on forming a startup team involving professors, researchers and market discovery activities. Taking these characteristics into account, this study empirically verified the impact of social capital from a network perspective and founding team diversity on I-Corps start-up performance. As a result of the analysis, the educational diversity of the founding team had a negative (-) effect on the financial performance of the founding team. On the other side, the gender diversity and the cognitive dimension of social capital had a positive (+) effect on the financial performance of the founding team. This study is expected to provide more useful theoretical and practical implications regarding the diversity, social capital, and performance interpretation of the I-Corps Lab startup team.

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The Concentration of Economic Power in Korea (경제력집중(經濟力集中) : 기본시각(基本視角)과 정책방향(政策方向))

  • Lee, Kyu-uck
    • KDI Journal of Economic Policy
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    • v.12 no.1
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    • pp.31-68
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    • 1990
  • The concentration of economic power takes the form of one or a few firms controlling a substantial portion of the economic resources and means in a certain economic area. At the same time, to the extent that these firms are owned by a few individuals, resource allocation can be manipulated by them rather than by the impersonal market mechanism. This will impair allocative efficiency, run counter to a decentralized market system and hamper the equitable distribution of wealth. Viewed from the historical evolution of Western capitalism in general, the concentration of economic power is a paradox in that it is a product of the free market system itself. The economic principle of natural discrimination works so that a few big firms preempt scarce resources and market opportunities. Prominent historical examples include trusts in America, Konzern in Germany and Zaibatsu in Japan in the early twentieth century. In other words, the concentration of economic power is the outcome as well as the antithesis of free competition. As long as judgment of the economic system at large depends upon the value systems of individuals, therefore, the issue of how to evaluate the concentration of economic power will inevitably be tinged with ideology. We have witnessed several different approaches to this problem such as communism, fascism and revised capitalism, and the last one seems to be the only surviving alternative. The concentration of economic power in Korea can be summarily represented by the "jaebol," namely, the conglomerate business group, the majority of whose member firms are monopolistic or oligopolistic in their respective markets and are owned by particular individuals. The jaebol has many dimensions in its size, but to sketch its magnitude, the share of the jaebol in the manufacturing sector reached 37.3% in shipment and 17.6% in employment as of 1989. The concentration of economic power can be ascribed to a number of causes. In the early stages of economic development, when the market system is immature, entrepreneurship must fill the gap inherent in the market in addition to performing its customary managerial function. Entrepreneurship of this sort is a scarce resource and becomes even more valuable as the target rate of economic growth gets higher. Entrepreneurship can neither be readily obtained in the market nor exhausted despite repeated use. Because of these peculiarities, economic power is bound to be concentrated in the hands of a few entrepreneurs and their business groups. It goes without saying, however, that the issue of whether the full exercise of money-making entrepreneurship is compatible with social mores is a different matter entirely. The rapidity of the concentration of economic power can also be traced to the diversification of business groups. The transplantation of advanced technology oriented toward mass production tends to saturate the small domestic market quite early and allows a firm to expand into new markets by making use of excess capacity and of monopoly profits. One of the reasons why the jaebol issue has become so acute in Korea lies in the nature of the government-business relationship. The Korean government has set economic development as its foremost national goal and, since then, has intervened profoundly in the private sector. Since most strategic industries promoted by the government required a huge capacity in technology, capital and manpower, big firms were favored over smaller firms, and the benefits of industrial policy naturally accrued to large business groups. The concentration of economic power which occured along the way was, therefore, not necessarily a product of the market system. At the same time, the concentration of ownership in business groups has been left largely intact as they have customarily met capital requirements by means of debt. The real advantage enjoyed by large business groups lies in synergy due to multiplant and multiproduct production. Even these effects, however, cannot always be considered socially optimal, as they offer disadvantages to other independent firms-for example, by foreclosing their markets. Moreover their fictitious or artificial advantages only aggravate the popular perception that most business groups have accumulated their wealth at the expense of the general public and under the behest of the government. Since Korea stands now at the threshold of establishing a full-fledged market economy along with political democracy, the phenomenon called the concentration of economic power must be correctly understood and the roles of business groups must be accordingly redefined. In doing so, we would do better to take a closer look at Japan which has experienced a demise of family-controlled Zaibatsu and a success with business groups(Kigyoshudan) whose ownership is dispersed among many firms and ultimately among the general public. The Japanese case cannot be an ideal model, but at least it gives us a good point of departure in that the issue of ownership is at the heart of the matter. In setting the basic direction of public policy aimed at controlling the concentration of economic power, one must harmonize efficiency and equity. Firm size in itself is not a problem, if it is dictated by efficiency considerations and if the firm behaves competitively in the market. As long as entrepreneurship is required for continuous economic growth and there is a discrepancy in entrepreneurial capacity among individuals, a concentration of economic power is bound to take place to some degree. Hence, the most effective way of reducing the inefficiency of business groups may be to impose competitive pressure on their activities. Concurrently, unless the concentration of ownership in business groups is scaled down, the seed of social discontent will still remain. Nevertheless, the dispersion of ownership requires a number of preconditions and, consequently, we must make consistent, long-term efforts on many fronts. We can suggest a long list of policy measures specifically designed to control the concentration of economic power. Whatever the policy may be, however, its intended effects will not be fully realized unless business groups abide by the moral code expected of socially responsible entrepreneurs. This is especially true, since the root of the problem of the excessive concentration of economic power lies outside the issue of efficiency, in problems concerning distribution, equity, and social justice.

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The Difference Analysis between Maturity Stages of Venture Firms by Classification Techniques of Big Data (빅데이터 분류 기법에 따른 벤처 기업의 성장 단계별 차이 분석)

  • Jung, Byoungho
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.15 no.4
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    • pp.197-212
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    • 2019
  • The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.