• Management & Information Systems Review
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• v.34 no.5
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• pp.23-47
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• 2015

In the Korea stock market, individual investors have perceived stock as short arbitrage investment, not long-term investment strategy. In order to reinforce stock market transparency and soundness, it is important to enforce the measures for stock market management. Especially, stock market event caused by financial policy can be given individual investors negative information regarding a stock trading. Thus, it is a need for investigating whether comprehensive review of listing eligibility is influenced on individual investors' responses and stock behaviors in respect of effectiveness. The purpose of this study to examine the relations between such stock market management and transitional aspect of individual investors' trading types and response on the based of pre- and post-event occurrence. Using an dataset of user's text messages on 9 firms posted on the firm-based social media (i.e., Naver, Daum, Paxnet) over the period 2009 to 2014. And we performed text-clustering and topic modeling according to keywords for classifying into investors group and non-investors groups and two types of investors were categorized depending on main topic transition by event windows in Comprehensive review of listing eligibility. The results indicated that a variety of stockholders existed in the stock. And the ratio of non-investors group was on the decrease, on the other hand, the proportion of investors group veer onto the side of pre-pattern after comprehensive review of listing eligibility. A distinctive feature of our study is to explain the influence of stock market management on response changes of individual investors as well as to categorize in accordance with time progression. Implications an suggestions for future research were also discussed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.111-117
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• 1992

In the stock marketing. investor needs speedy and accurate decision making for the investment. A stock exchange index provides the important index of the early of 1993 in Korea using Fuzzy Delphi Method(F. D. M) which is widely used to a mid and long range forecasting in decision making problem. In the Fuzzy Delphi method, considerably qualified experts an first requested to give their opinion seperately and without intercommunication. The forecasting data of experts consist of Triangular Fuzzy Number (T.F.N) which represents the pessimistic, moderate, and optimistic forecast of a stock exchange index. A statistical analysis and dissemblance index are then made of these subject data. These new information are then transmitted to the experts once again, and the process of reestimation is continued until the process converges to a reasonable stable forecast of stock exchange index. The goal of this research is to forecast the stock exchange index using F.D.M. in which subjective data of experts are transformed into quasi -objective data index by some statistical analysis and fuzzy operations. (a) A long range forecasting problem must be considered as an uncertain but not random problem. The direct use of fuzzy numbers and fuzzy methods seems to be more compatible and well suited. (b) The experts use their individual competency and subjectivity and this is the very reason why we propose the use of fuzzy concepts. (c) If you ask an expert the following question: Consider the forecasting of the price index of stocks in the near future. This experts wi11 certainly be more comfortable giving an answer to this question using three types of values: the maximum value, the proper value, and the minimum value rather than an answer in terms of the probability.

• Information Systems Review
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• v.22 no.4
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• pp.135-161
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• 2020

With the growth of blockchain and cryptocurrency-related markets, cryptocurrency exchanges are growing as a new industry. However, as the legal and regulatory definitions of cryptocurrencies are still in progress, unlike existing industrial groups, they are not under the supervision of regulatory agencies. As a result, users (i.e., cryptocurrency investors) have suffered two types of damage that could occur from hacking and other accidents on the exchanges. One type of the damage is the loss of assets caused by the extortion of personal information or account and the other is the damage from users who might be involved in external frauds. Both are analyzed in comparison with existing operators whose functions are like the exchanges. The results of this study show that membership (KYC: Know Your Client), log-in, and additional authentication in transactions are on the similar level to those of the operators while the fraud detection system (FDS) and anti-money laundering (AML) of fiat currencies and cryptocurrencies need rapid improvement.

• Management & Information Systems Review
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• v.36 no.3
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• pp.255-268
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• 2017

This study examines whether firms with tax avoidance of Corporate Social Responsibility(CSR) performance is tempered by the extent firms engage in CEO turnovers. Considering the increasing interest in CSR activities of the firm to secure sustainable growth of national economy, this paper investigates the benefit and cost of CSR activities by combining the agency theory using the firm level data. Prior studies document that investors positively value tax avoidance. The rationale for this finding is that tax avoidance provides cash savings that can be used by firm managers to generate future shareholder wealth. Prior studies also show that investors' valuations are sensitive to the risk of future negative tax outcomes. Assuming that many types of CSR performances are low risk, low yielding uses of firm resources, we posit that higher levels of CSR performance may signal to investors that cash generated via tax avoidance has not been fully used to generate a return sufficient to offset the risk associated with aggressive tax planning strategies. Consistent with this argument, we predict and find that the positive association between CSR performance and tax avoidance is significantly weakened when firms have higher positive levels of CEO turnovers. Further, we predict and find that 'philanthropic' types of CSR activities in particular are associated with investor discounting of tax avoidance. We interpret our results as suggesting the equity market views CSR activities to be ostensibly funded through cash savings generated via tax avoidance.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.57-70
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• 2023

The investigation delved into 457 pieces of scholarly work, encompassing articles, published theses, and dissertations from the National Research Foundation of Korea, spanning the period of the 1997 IMF financial crisis up to 2022. The materials were sourced using terms such as 'angel investment', 'angel investor', and 'angel investment attraction'. The initial phase involved filtering out redundant entries from the preliminary collection of 267 works, leaving aside pieces that didn't pertain directly to angel investment as indicated in their abstracts. The next stage of the analysis involved a more rigorous selection process. Out of 43 papers earmarked in the preceding cut, only 32 were chosen. The criteria for this focused on the exclusion of conference presentations, articles that were either not submitted or inconclusive, and those that duplicated content under different titles. The final selection of 32 papers underwent a thorough systematic literature review. These documents, all pertinent to angel investment in South Korea, were scrutinized under five distinct categories: 1) publication year, 2) themes of research, 3) strategies employed in the studies, 4) participants involved in the research, and 5) methods of research utilized. This meticulous process illuminated the existing landscape of angel investment studies within Korea. Moreover, this study pinpointed gaps in the current body of research, offering guidance on future scholarly directions and proposing social scientific theories to further enrich the field of angel investment studies and analysis also seeks to pinpoint which areas require additional exploration to energize the field of angel investment moving forward. Through a comprehensive review of literature, this research intends to validate the establishment of future research trajectories and pinpoint areas that are currently and relatively underexplored in Korea's angel investment research stream. This study revealed that current research on domestic angel investment is concentrated on several areas: 1) the traits of angel investors, 2) the motivations behind angel investing, 3) startup ventures, 4) relevant institutions and policies, and 5) the various forms of angel investments. It was determined that there is a need to broaden the scope of research to aid in enhancing and stimulating the scale of domestic angel investing. This includes research into performance analysis of angel investments and detailed case studies in the field. Furthermore, the study emphasizes the importance of diversifying research efforts. Instead of solely focusing on specific factors like investment types, startups, accelerators, venture capital, and regulatory frameworks, there is a call for research that explores a variety of associated variables. These include aspects related to crowdfunding and return on investment in the context of angel investing, ensuring a more holistic approach to research in this domain. Specifically, there's a clear need for more detailed studies focusing on the relationships with variables that serve as dependent variables influencing the outcomes of angel investments. Moreover, it's essential to invigorate both qualitative and quantitative research that delves into the theoretical framework from multiple perspectives. This involves analyzing the structure of variables that have an impact on angel investments and the decisions surrounding these investments, thereby enriching the theoretical foundation of this field. Finally, we presented the direction of development for future research by confirming that the effect on the completeness of the business plan is high or low depending on the satisfaction of the entrepreneurs in addition to the components.

• International Journal of Knowledge Content Development & Technology
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• v.10 no.3
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• pp.33-50
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• 2020

Knowledge recipes are packages of knowledge which arise from the process of combining the knowledge assets in the organization in distinctive ways. This involves converting them into useful outputs which are the ideal core competitive advantage enablers for companies. The major objective of this study was to propose a knowledge recipe for financial-sector state corporations in Kenya. The study adopted a convergent parallel mixed methods research design. Quantitative and qualitative data were collected using questionnaires and key informant interviews. The target population of the study was 1574 respondents drawn from all financial state corporations. A multistage sampling technique was used for the study. The first phase involved purposive sampling of the organizations to be studied whereby the four state corporations namely: Capital Markets Authority, Competition Authority of Kenya, Kenya Investment Authority, and Kenya Revenue Authority were identified. The second phase entailed stratified sampling of the respondents in three strata namely senior management team, knowledge management team, and general staff. The authors used a census of all senior management team and knowledge management staff while a simple random sampling technique was used for the general staff. By use of the Krejcie and Morgan table, the actual sample size was 358 respondents from all the four organizations. Data were collected using questionnaires and interview schedules. The qualitative data were analyzed using content analysis while the quantitative data were analyzed by the use of Ms. Excel and VOSviewer and presented using pie charts, bar graphs, and tables. The response rate for this study was 257 (72%). The study revealed that while most employees in the financial sector organizations understand their knowledge needs, knowledge types, knowledge uses and knowledge gaps, they do not have a universal knowledge recipe to facilitate effective knowledge management in their organizations. Consequently, the authors propose a universal knowledge recipe for the state corporations in the financial sector in Kenya. The ingredients of the recipe are legal-knowledge (18%), financial knowledge (15%), administrative knowledge (11%), best practice (10%), lessons learnt (8%), human resource knowledge (8%), research and statistics knowledge (7%), product knowledge (6%), policy and procedure knowledge (5%), ICT knowledge (4%), investor knowledge (3%), markets knowledge (2%), general knowledge (2%) and regulatory framework knowledge (1%).

• Korean small business review
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• v.43 no.4
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• pp.173-209
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• 2021

Start-up accelerators are a new type of investor that provide mentoring, education and seed capital to start-ups for a fixed duration and receive a certain stake in them in return. They help start-ups achieve successful commercialization. With increase in performance visibility, the number of private and public sector accelerators rose across domestic and international markets. Private sector accelerators are established and operated by private entities while public sector accelerators are established and operated by the government. Both play complementary roles that are becoming increasingly important to start-ups. Therefore, this study aims to examine the differences in major operational goals and investment determinants between private and public sectors and to understand their implications. The results show that the private sector prioritizes profit generation through the investment, while the public sector aims to contribute to the development of high-growth start-ups, and create region-specific and technology-specific start-up ecosystems. Additionally, both groups consider customer needs the most important determinant. Public groups are more conservative in investments and tend to place importance on objective indicators such as patents, partners, mentors, and co-founders. Conversely, private groups value the capabilities of founders and their ease of collaboration with accelerators. These findings can help start-ups get support from public or private accelerators more easily. It will also help public and private accelerators refine the criteria for selecting start-ups.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.360-373
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• 2017

The BTO-a projects is the types, which has a demand risk among the type of PPP projects in Korea. When demand risk is realized, private investor encounters financial difficulties due to lower revenue than its expectation and the government may also have a problem in stable infrastructure operation. In this regards, the government has applied various risk sharing policies in response to demand risk. However, the amount of government's risk sharing is the government's contingent liabilities as a result of demand uncertainty, and it fails to be quantified by the conventional NPV method of expressing in the text of the concession agreement. The purpose of this study is to estimate the value of investment risk sharing by the government considering the demand risk in the profit sharing system (BTO-a) introduced in 2015 as one of the demand risk sharing policy. The investment risk sharing will take the form of options in finance. Private investors have the right to claim subsidies from the government when their revenue declines, while the government has the obligation to pay subsidies under certain conditions. In this study, we have established a methodology for estimating the value of investment risk sharing by using the Black - Scholes option pricing model and examined the appropriateness of the results through case studies. As a result of the analysis, the value of investment risk sharing is estimated to be 12 billion won, which is about 4% of the investment cost of the private investment. In other words, it can be seen that the government will invest 12 billion won in financial support by sharing the investment risk. The option value when assuming the traffic volume risk as a random variable from the case studies is derived as an average of 12.2 billion won and a standard deviation of 3.67 billion won. As a result of the cumulative distribution, the option value of the 90% probability interval will be determined within the range of 6.9 to 18.8 billion won. The method proposed in this study is expected to help government and private investors understand the better risk analysis and economic value of better for investment risk sharing under the uncertainty of future demand.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.109-124
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• 2024

The value of platform companies is rapidly increasing, exerting significant influence across industries. Identifying and fostering promising platform companies is crucial for enhancing national competitiveness. Consequently, tailored evaluation standards are necessary for such companies. This study derived investment decision factors specific to platform companies and compared the importance of each factor using Analytic Hierarchy Process (AHP) analysis. Key factors included platform characteristics, finance, entrepreneur (team), market, and product/service attributes. The findings revealed that platform characteristics were deemed the most crucial factor for investors. Specifically, factors such as platform size, ease of value fixation, core participant group, and data value were identified as pertinent for evaluating platform companies. Moreover, analysis distinguished between investors with prior platform investment experience and those without. Significantly, investors with platform investment experience placed greater emphasis on the value of data secured by platform Furthermore, it was observed that investors prioritized future value and growth potential over current value when investing in platform. Notably, founder/team characteristics, typically highly regarded in previous studies, ranked lower in importance in this study, highlighting a shift in focus. The discrepancy between this study's results and prior research on investment decision factors is attributed to the specificity of the questions posed. By focusing on investment decision factors for platform startups rather than generic startup inquiries, investor responses aligned more closely with platform-focused considerations. Given the burgeoning venture investment landscape, there's a growing need for detailed research on startups within specific sectors like IT, travel, and biotech. This approach can replace extensive research covering all startup types to identify investment decision factors suited to the characteristics of each individual industry.