• The Korean Journal of Financial Management
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• v.20 no.1
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• pp.233-260
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• 2003

This paper compares the underpricing of IPOs listed on KOSADQ with that of IPOs listed on KSE. When we consider the last day of upper price limit of IPOs, IPOs on KSE show higher initial excess return than IPOs on KOSDAQ. And AR2 which is the abnormal return based on the stock price of the last day recording upper limit after listing, IPOs on KOSDAQ exhibit larger abnormal return than IPOs on KSE. Our study also reports that the long-term performance of IPOs in two markets does not show any difference. That is, IPOs of both markets under performed in the long-run. The wealth relatives of IPOs are a little higher than market portfolio. We explored the reasons of the underpricing of IPOs in both markets through the multiple regression analysis. The business history is examined asstatistically significant variable to explain the underpricing.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.2
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• pp.47-65
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• 2020

With the era of the Fourth Industrial Revolution in sight, innovative business models utilizing new technologies are emerging, and startups are enjoying an abundance of opportunities based on the agility to respond to disruptive innovations and the opening to new technologies. However, what is most important in creating a sustainable start-up ecosystem is not the start-up itself, but the process of research-start-investment-investment-the leap to listing and big business-in order to build a virtuous circle of startups that leads to re-investment. To this end, the environment created in the hub area where start-ups were conducted is important, and these material and non-material environmental factors are described as being inclusive by the word "entrepreneurial ecosystem." This study aims to provide implications for Korea's entrepreneurial ecosystem through the study of the interaction of the elements that make up the start-up ecosystem and the relationship of ecosystem participants in Singapore. Singapore has been consistently mentioned as the top two Asian countries in assessing the start-up environment and business environment. In this process, six elements of the entrepreneurial ecosystem presented by Isenberg(2010)-policies, finance, culture, support, human resources, and market-are the best frameworks for analyzing entrepreneurial ecosystems in terms of well encompassing prior studies related to entrepreneurial ecosystem elements, and a model of regional transformation is formed focusing on some elements to suit Singapore, the target area of study. By considering that Singapore's political nature would inevitably have a huge impact on finance, Smart Nation policy was having an impact on university education related to entrepreneurship, and that the entrepreneurial networks and global connectivity formed within Singapore's start-up infrastructure had a significant impact on Singapore's start-up's performance, researches needed to look more at the factors of policy, culture and market. In addition, qualitative research of participants in the entrepreneurial ecosystem was essential to understand the internal interaction of the elements of the start-up ecosystem, so the semi-structured survey was conducted by visiting the site. As such, this study examined the status of the local entrepreneurial ecosystem based on qualitative research focused on policies, culture and market elements of Singapore's start-up ecosystem, and intended to provide implications for regulations related to start-ups, the role of universities and start-up infrastructure through comparison with Korea. This could contribute not only to the future research of the start-up ecosystem, but also to the creation of a start-up infrastructure, boosting the start-up ecosystem, and the establishment of the orientation of the start-up education in universities.