• Information Systems Review
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• v.23 no.3
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• pp.97-125
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• 2021

Blockchain is a core technology to solve personal information leakage and data management issues, which are limitations of existing Genomic Sequencing services. Due to continuous cost reduction and deregulation, the market size of Genomic Sequencing has been increasing, also the potential of services is expected to increase when Blockchain's security and connectivity are combined. We created our research model by combining the Technology Acceptance Model (TAM) and the Innovation Resistance Theory also analyzed the factors affecting the acceptance intention and innovation resistance of the Blockchain Based Genomic Sequencing Information Platform. A survey was conducted on 150 potential users of Blockchain and Genomic Sequencing services. The analysis was conducted by setting the four Blockchain variables: Security, transparency, availability, and diversity). Also, we set the Perceived Usefulness, Perceived risk, and Perceived Complexity for Technology Acceptance and Innovation Resistance variables and analyzed the effect of the characteristics of the Blockchain on acceptance intention and innovation resistance through these variables. Through this analysis, key variables that need to be considered important to reduce resistance and increase acceptance intention could be identified. This study presents innovation factors that should be considered in companies preparing a new Blockchain Based Genomic Sequencing Information Platform.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.133-145
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• 2023

This paper examines whether investors have adapted to the venture capital(VC) investment style. VC firms invest in privately held companies and generate returns by selling them after the lock-up period expires. We analyze the impact on stock prices before and after the lock-up period expiration, and compare the Cumulative Abnormal Return(CAR) between the past period(2015-2017) and the recent period(2020-2022) to investigate the effect of the second venture boom. The main findings are as follows. First, unlike in the past, stock price returns around the lock-up period expiration have been lower than the KOSDAQ index in recent years. Second, the impact on stock prices is significant for both 1-month and 12-month lock-up periods. Specifically, it is confirmed that stocks held by venture capital and professional investors with a 1-month lock-up period respond in advance to their information after the second venture boom. Finally, we find that there is a difference in CAR depending on whether or not the company received VC investment after the second venture boom. Based on our findings, we suggest that VC firms need to revise their exit strategies to improve performance. This includes finding ways to reduce information asymmetry and fees, as well as developing strategies to mitigate market volatility. Additionally, the current lock-up period for VCs should be reconsidered as it may increase the risk of stock price decline. We recommend that the government revise the scope and duration of lock-up periods to protect investors after IPO.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.153-158
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• 2024

Recently, concerns about the spread of credit risk in China's real estate market are gradually increasing. Therefore, it is very meaningful to diagnose the management stability of Chinese commercial banks. This study analyzes the impact of housing prices on the loan proportion and management stability of Chinese commercial banks. In addition, we classify Chinese commercial banks according to size and verify whether there are differences in loan proportion and management stability. If there is a difference by scale, the effect of interaction with housing price changes is also verified. The analysis results are summarized as follows. First, it was found that as the housing price growth rate increases, the proportion of loans from Chinese commercial banks increases. Second, as the rate of increase in housing prices and the proportion of total loans increases, management stability appears to decrease. Third, larger banks were found to have a higher proportion of loans, and smaller banks were found to have greater management stability. The results of this analysis show that Chinese commercial banks' aggressive expansion of their loan proportion is lowering their management stability. Therefore, it is necessary to adjust the loan ratio to the appropriate size level and secure stability with differentiated strategies according to the loan ratio

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• The Korean Journal of Air & Space Law and Policy
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• v.24 no.2
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• pp.69-104
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• 2009

This study is to know the Enhancement of Aviation Safety for Low Cost Carrier in Korea through the long and mid term air safety policy. Especially, the aviation safety authorities of the developed countries in aviation establish action plans under the system plan of central government. Then the countries implement those plans systematically to the related aviation business so that they promote efficient air safety policy implementation. At this time, the Korean government should present the vision about an air safety and systematic strategic plan to cope with the future aviation industry change. Also, it is needed to establish a specific aviation safety action plan. Namely, an air safety master plan and long-term road map must be established. This paper deduces some implications through the abroad cases of aviation safety plan, and then tries to find the applying method of the implications to Korea in the rapidly changing aviation market in the 21st century. It is expected that this paper will help the Korean aviation industry to play a major role in the future. In oder to get suggestions aviation policies of advanced countries with regard to aviation safety, we have looked at the aviation policies of the U.S., the U.K., Australia and Japan, and also LCC's states overseas, LCC's safety policies in Korea, and aviation safety status. Since existing LCCs and new LCCs based in Korea have become the new concept, this new market for LCC has been booming recently. Around Southeast Asia, while there are some LCCs including Air Asia which is supported by the government of Malaysia with emphasis on safety, there are other LCCs, which have failed to achieve confidence in safety and have led to aircraft accidents and financial mismanagement, so we need to verify the safety of overseas LCCs, try to improve domestic LCCs in order to fly international routes and aid international aviation safety. LCCs have been increasing lately thanks to open skies policy and a wide variety of flights.lines. Air Busan, Jin Air, Jeju air, Eastar Air are in service. so the risk of new potential hazards may increase. Therefore it is necessary to take the initiative in aviation markets inside and outside of Korea and the safety management of new LCCs should be taken more seriously than ever before. Among overseas aviation safety policies, we need to implement the FAA's Filght Plan which has a specific Business Plan. I hope this thesis will help improve aviation safety locally and internationally.

• The Korean Journal of Air & Space Law and Policy
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• v.35 no.2
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• pp.375-402
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• 2020

The adoption and entering into force of the Registration Convention was another achievement in expanding and strengthening the corpus iuris spatialis. It was the fourth treaty negotiated by the member states of the UNCOPUOS and it elaborates further Articles 5 and 8 of the Outer Space Treaty(OST). The Registration Convention also complements and strengthens the Article 11 of the OST, which stipulates an obligation of state parties to inform the UN Secretary-General of the nature, conduct, locations, and results of their space activities in order to promote international cooperation. The prevailing purposes of the Registration Convention is the clarification of "jurisdiction and control" as a comprehensive concept mentioned in Article 5 8 of the OST. In addition to its overriding objective, the Registration Convention also contributes to the promotion and the exploration and use of outer space for peaceful purposes. Establishing and maintaining a public register reduces the possibility of the existence of unidentified space objects and thereby lowers the risk such as, for example, putting the weapons of mass destruction secretly into orbit. And furthermore it could serve for a better space traffic management. The Registration Convention is a treaty established to implement Article 5 of OST for the rescue and return of astronaut in more detail. In this respect, if OST is a general law, the Registration Convention would be said to be in a special law. If two laws conflict the principle of lex specialis will be applied. Countries that have not joined the Registration Convention will have to follow the rules concerning the registration of paragraph 7 of the Declaration by the United Nations General Assembly resolution 1721 (X V I) in 1961. UN Resolution 1721 (XVI) is essentially non-binding, but appears to have evolved into the norm of customary international law requiring all States launching space objects into orbit or beyond to promptly provide information about their launchings for registration to the United Nations. However, the nature and scope of the information to be supplied is left to the discretion of the notifying State. The Registration Convention is a treaty created for compulsory registration of space objects by nations, but in reality it is a treaty that does not deviate from existing practice because it is based on voluntary registration. With the situation of dealing with new problems due to the commercialization and privatization of the space market, issues related to the definition of a 'space object', including matter of the registry state of new state that purchased space objects and space debris matter caused by the suspension of space objects launched by the registry state should be considered as matters when amendments, additional protocols or new Registration Convention are established. Also the question of registration of a flight vehicle in the commercial space market using a space vehicle traveling in a sub-orbital in a short time should be considered.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.1
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• pp.61-72
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• 2018

In a rapidly changing environment, such as globalization, technology-based startups are attracting attention as a new growth engine that creates jobs and added value and promotes national competitiveness. At present, countries around the world recognize the development of technology-based start-up companies as a major policy task and strive to make policy efforts to revitalize start-ups and strengthen innovation capabilities of companies. Especially, in order to secure superiority in the fierce market competition, it is becoming more and more important for the growth and development of technological start-up companies that pioneer new markets and energize the economy based on original and innovative technologies. Therefore, it is necessary to study systematically and plan for survival and growth of technology start-up companies. The purpose of this study is to investigate the entrepreneurial spirit of Innovation, Entrepreneurship, Risk Sensibility and Technology Innovation Capacity, R&D ability, Technology Accumulation Capacity, Technology Innovation System, The results of this study are as follows. the effects of marketing ability on technical performance and financial performance are examined. First, the CEO 's entrepreneurial spirit has an effect on the technical performance and financial performance of the management performance. Second, the technology accumulation ability and the R & D capability have a positive effect on the technical performance. Finally, it was found that the ability to commercialize the technology commercialization capacity affects both technical performance and financial performance. The policy implications that can be gained through this are as follows. First, by strengthening cooperation between universities and research institutes, related technology entrepreneurship education programs should be upgraded so that technology entrepreneurs or preliminary entrepreneurs can capture business opportunities and secure market price competitiveness. Secondly, R & D for the purpose of start-up should be developed and marketable technology should be developed and linked to direct start-up. Third, it is necessary to activate the program to match the company with the honorary retirement manpower of large enterprises and SMEs, which have more experience in field experience than the founders.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Management & Information Systems Review
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• v.29 no.2
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• pp.1-25
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• 2010

This study attempted to empirically test the determinants of stock returns in Korean stock market applying multi-factor model proposed by Haugen and Baker(1996). Regression models were developed using 16 variables related to liquidity, risk, historical price, price level, and profitability as independent variables and 690 stock monthly returns as dependent variable. For the statistical analysis, the data were collected from the Kis Value database and the tests of forecasting power in this study minimized various possible bias discussed in the literature as possible. The statistical results indicated that: 1) Liquidity, one-month excess return, three-month excess return, PER, ROE, and volatility of total return affect stock returns simultaneously. 2) Liquidity, one-month excess return, three-month excess return, six-month excess return, PSR, PBR, ROE, and EPS have an antecedent influence on stock returns. Meanwhile, realized returns of decile portfolios increase in proportion to predicted returns. This results supported previous study by Haugen and Baker(1996) and indicated that firm-characteristic model can better predict stock returns than CAPM. 3) The firm-characteristic model has better predictive power than Fama-French three-factor model, which indicates that a portfolio constructed based on this model can achieve excess return. This study found that expected return factor models are accurate, which is consistent with other countries' results. There exists a surprising degree of commonality in the factors that are most important in determining the expected returns among different stocks.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.