• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.11
• /
• pp.5476-5487
• /
• 2013

From foreign and/or domestic investors' perspectives, it may be interesting to find any financial attributes or profile of the firms headquartered in 'Chungcheong' province concerning profitability, given that this subject so far drew less attention in the previous literature. This study performed three hypothesis tests on the profitability indicator by utilizing the models such as the 'panel data' one and the 'logistic' regression one, applying a modified 'Dupont' system. With respect to the major findings, the results identified that the proxies measuring leverage across the book-value(BVLEV1) and the market-value(MVLEV1) bases, were statistically significant constituents determining profitability. Another explanatory variable, SIZE, with its positive and statistically significant relationship to the indicator, represented that the firms in the province were smaller than their counterparts in the other regional areas in Korea. DRELY applying a modified 'Dupont' system, found to be the only statistically significant discriminating factor between these comparison groups. As one of the primary contributions of this study, the outcomes may be used by the financial institutions operated across the regions including Seoul Metropolitan area, when implementing their lending practices to provide funds for potential borrowers such as the firms belonging to 'Chungcheong' province.

• Journal of Digital Convergence
• /
• v.15 no.2
• /
• pp.135-144
• /
• 2017

This study is to identify the growth rate and volatility of logistics related firms in the stock market. To do this, we used monthly data for 197 years from June 2000 to October 2016 by selecting KOSPI and Transport & Storage(T&S), KOSDAQ, Transportation(TRANS) index. The purpose of this study is to compare the T&S and TRANS stock index returns with the KOSPI and KOSDAQ index. And we are to judge whether the development potential of the logistics industry and the value of the investment of related companies in the future is high. For this purpose, we will analyze the basic statistics, correlation and growth rate of each index, and compare T&S and TRANS with market returns. Analysis result, for the past 197 months logistics related T&S and TRANS have been higher than market returns. The correlation was highly related to TRANS and T & S in KOSPI, but it was not related to KOSDAQ. TRANS represents high risk and high return, while KOSDAQ represents high risk and low return market. TRANS is considered to be an efficient investment. We expect the future development of logistics related industries and T & S and TRANS to show a high rate of increase compared to the market returns.

• Journal of Technology Innovation
• /
• v.14 no.1
• /
• pp.67-99
• /
• 2006

Utilizing the patent application data between 1997 and 2002, this study focuses on analysing the impact of patents on firm value. Especially we attempt to examine the difference of patents between venture firms and general firms. This paper first shows that the number of the patent applications of general firms listed on the securities market is more than that of venture firms listed on KOSDAQ. It is thought that this result is originated from the facts that the size of firms of the securities market is usually bigger than that of the firms of KOSDAQ and that these large firms could manage R&D more efficiently. Second, this paper reports that there is no difference in the ratio of patent maintenance between venture firms and general firms. Both venture firms and non-venture firms would do their best to keep their patents after patent regisration. Third, in the regression of patent index and firm growth, we find that the excellence of patent and the number of patents per employee would have an impact on the growth of firms. Fourth, the regression of patent index and profitability shows that the excellence and the number of patents per employee might have an effect on the profitability of firms.

• The Korean Journal of Applied Statistics
• /
• v.27 no.1
• /
• pp.159-168
• /
• 2014

The news impact curve(NIC) originally proposed by Engle and Ng (1993) is a graphical representation of volatility for financial time series. The NIC is a simple but a powerful tool for identifying variability of a given time series. It is noted that the NIC is suited to symmetric volatility. Recently a lot of attention has been paid to asymmetric volatility models and therefore asymmetric version of the NIC would be useful in the field of financial time series. In this article, we propose to incorporate LASSO in constructing asymmetric NICs based on asymmetric GARCH models. In particular, bilinear GARCH models are considered and illustrated via KOSDAQ data.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.2
• /
• pp.878-884
• /
• 2012

This study presents a portfolio selection strategy focusing on construction corporations by taking into accounts managerial efficiency and growth potential of a company. Data envelopment analysis(DEA) methodology and dividend scoring table are adopted for evaluating the managerial efficiency and growth potential of a company respectively. In order to show the effectiveness of the portfolios selected by the strategies proposed in this study, we constructed 3 portfolios for every 4 years (2007-2010) out of 56 listed construction corporations in KOSPI and KOSDAQ, and proved that our portfolios are superior to benchmark portfolios in terms of portfolio evaluation measures.

• Journal of Information Technology Services
• /
• v.15 no.3
• /
• pp.1-14
• /
• 2016

According to the growing importance of science and technology policy, investment in research and development (R&D) has been increased. In this context, a patent as one of outcome of firm's systematic R&D investment is the way to hold a dominant position in companies' technology competitiveness and also to protect technology right. This study attempts to find the effects of input resources and intellectual property production activity on company value. It conducted empirical analysis based on 238 KOSDAQ-listed and medium-sized firms. Using the previous research, this study configurated research variables about activity of patent production, company value, and input of resources. Through these variables, it aims to know the effect of input of resources and activity of patent production on company value. First, the result of regression analysis shows that R&D cost has the positive effect on a patent production and sales promotion cost positively affects on the registration of the trademark. Second, the output of regression analysis indicates that a patent has the positive effect on company value but a trademark has not. With regard to the input of resources, R&D cost has the influence on company value but sales promotion cost has not. This study attempts to find the effectiveness of company's intellectual property registration on its value and suggests a way that the systematic R&D investment contributes the growth of company value.

• Journal of Intelligence and Information Systems
• /
• v.7 no.2
• /
• pp.95-115
• /
• 2001

We propose classifying the venture firms into four types of knowledge management. For this purpose, we collected questionnaire data from 101 venture firms listed in KOSDAQ, and applied a unsupervised neural network algorithm SOM to obtain four clusters representing knowledge management types-High Tech Type, Organizational Knowledge Type, Information Technology Type, and Beginner Type. Based on the results, we conclude that the venture firms listed in KOSDAQ should first know its own knowledge management type, and then apply appropriate strategies to take advantage of the knowledge management impacts on the competitiveness.

• The Journal of Information Systems
• /
• v.27 no.3
• /
• pp.123-140
• /
• 2018

Purpose The purpose of this study is to explore the optimal trading frequency which is useful for stock price prediction by using deep learning for charting image data. We also want to identify the appropriate time for accurate forecasting of stock price when performing pattern analysis. Design/methodology/approach In order to find the optimal trading frequency patterns and forecast timings, this study is performed as follows. First, stock price data is collected using OpenAPI provided by Daishin Securities, and candle chart images are created by data frequency and forecasting time. Second, the patterns are generated by the charting images and the learning is performed using the CNN. Finally, we find the optimal trading frequency patterns and forecasting timings. Findings According to the experiment results, this study confirmed that when the 10 minute frequency data is judged to be a decline pattern at previous 1 tick, the accuracy of predicting the market frequency pattern at which the market decreasing is 76%, which is determined by the optimal frequency pattern. In addition, we confirmed that forecasting of the sales frequency pattern at previous 1 tick shows higher accuracy than previous 2 tick and 3 tick.

• The Korean Journal of Applied Statistics
• /
• v.21 no.3
• /
• pp.537-546
• /
• 2008

One of main goals of time series analysis is to estimate prediction of future values. In this paper, we investigate the bias problem when the transformation and back- transformation approach is applied in ARMA models and introduce a modified smearing estimation to reduce the bias. An empirical study on the returns of KOSDAQ index via Yeo-Johnson transformation was executed to compare the performance of existing methods and proposed methods and showed that proposed approaches provide a bias-reduced estimation of the prediction value.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.5
• /
• pp.1630-1648
• /
• 2021

Owing to the unprecedented COVID-19 pandemic, the pharmaceutical industry has attracted considerable attention, spurred by the widespread expectation of vaccine development. In this study, we collect relevant topics from news articles related to COVID-19 and explore their links with two South Korean pharmaceutical indices, the Drug and Medicine index of the Korea Composite Stock Price Index (KOSPI) and the Korean Securities Dealers Automated Quotations (KOSDAQ) Pharmaceutical index. We use generalized Dirichlet-multinomial regression (g-DMR) to reveal the dynamic topic distributions over metadata of index values. The results of our analysis, obtained using g-DMR, reveal that a greater focus on specific news topics has a significant relationship with fluctuations in the indices. We also provide practical and theoretical implications based on this analysis.