• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5663-5670
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• 2011

This study performed empirical analyses of the static trade-off theory and the pecking order theory which explain financing behavior of firms. The results of regression analyses using the data of 762 listed non-financing firms on the KOSDAQ market from 2000 to 2010 have shown mixed evidences supporting either the trade-off theory or the pecking order theory. Specifically, as the effective tax rate and the firm size increases, debt ratio increases, which is consistent with the trade-off theory. However as the growth opportunity and the profitability increases, debt ratio decreases, which is consistent with the pecking order theory.

• Journal of Industrial Convergence
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• v.7 no.1
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• pp.63-82
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• 2009

Many The purpose of this paper is to revisit the existence of monthly effect in the Korea Stock Market. We conducted additory test about KOSPI200 from January 1990 to December 2002 and about KOSDAQ from January 2002 to December 2006. The other main focus is examine Size Effect in Korean Stock Market. We also indicate Information hypothesis throught our findig. Data used in this paper are monthly returns of KOSPI and KOSDAQ from 1980 to 2006. As a result, Evidence is provided that monthly abnormal returns in January have large means relative to the remaining eleven months. The relation between abnormal returns and size is always negative and more pronounced in January than in any other month-even in years. More than fifty percent of the January premium is attributable to large abnormal returns during the first week of trading in the year particularly on the first trading day. This finding is highly significant in the mall sized capital stock of KOSPI market. We found January effect and Size Effect in the KOSPI market, but we didn't find January effect and Size Effect in the KOSDAQ market and KOSPI200.

• Asia-Pacific Journal of Business
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• v.13 no.2
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• pp.107-122
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• 2022

Purpose - This study aims to develop machine learning models to predict administrative issue designation in KOSDAQ Market using financial data. Design/methodology/approach - Employing four classification techniques including logistic regression, support vector machine, random forest, and gradient boosting to a matched sample of five hundred and thirty-six firms over an eight-year period, the authors develop prediction models and explore the practicality of the models. Findings - The resulting four binary selection models reveal overall satisfactory classification performance in terms of various measures including AUC (area under the receiver operating characteristic curve), accuracy, F1-score, and top quartile lift, while the ensemble models (random forest and gradienct boosting) outperform the others in terms of most measures. Research implications or Originality - Although the assessment of administrative issue potential of firms is critical information to investors and financial institutions, detailed empirical investigation has lagged behind. The current research fills this gap in the literature by proposing parsimonious prediction models based on a few financial variables and validating the applicability of the models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.571-580
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• 2017

Since the KONEX market was launched in 2013, many studies of the market have focused on policy reports and management of the market. In this study, we analyzed the impact of training and employee benefits expenses on business performance in the KONEX market in comparison with firms in the KOSDAQ 100. The expenses associated with employee training and benefits can have an overlapping power when explaining the business performance. To determine the net effect of each variable on business performance, we used regression by successive orthogonalization. The training and the employee benefits expenses in both markets showed a positive effect on business performance. However, in the KONEX market, we found that the lag effect of training expense to business performance was relatively smaller than in the KOSDAQ 100. This difference may be related to problems such as short continuous service and frequent turnover of SMEs in Korea, and implies that overall human resource management should be implemented to increase the efficiency of training expenses.

• The Korean Journal of Financial Management
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• v.20 no.1
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• pp.163-190
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• 2003

This study tests the hypothesis of market efficiency through the information spillover effects over price and volatility across countries by using open-to-close(daytime) returns and close-to-open(overnight) returns of NASDAQ, KOSDAQ and JASDAQ data from January 3, 1997 to December 21, 2000. Based on Granger-causality and time-varying AR(1)-GARCH(1, 1)-M models we document that the evidence of statistically significant conditional mean and volatility spillovers effects from the daytime returns and volatility of NASDAQ to the overnight returns and volatility of KOSDAQ is observed both before and after the IMF foreign currency crisis but not to the close-to-open return before the IMF foreign currency crisis. We can understand the information spillover effect from NASDAQ to KOSDAQ on the overnight rather than the daytime grows more significantly after the IMF foreign currency crisis. We also find the interactive information spillover effect between NASDAQ and JASDAQ both before and after the IMF financial crisis, in particular, to close-to-open return. In addition, the market efficiency between KOSDAQ and NASDAQ is on an increasing trend through IMF foreign currency crisis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.670-676
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• 2009

Bankruptcy of firms in Korea can cause distress of financial institutions because these institutions have disterssed bond. Accordingly, social and economical spill-over effects by these results are very big. Even after the difficult times of IMF crisis had ended, bankruptcy of information-based small-medium companies and venture firms listed on the KOSDAQ has been continued. In this context, this study developed and adopted failure prediction models for which discriminant analysis was used. Samples of this study was 81 firms respectively for both failed and non-failed firms listed on the KOSDAQ between the year of 2000 and 2007. The results of this study are as follows. First, the accuracy of classification of the model by years was $74.5%{\sim}76.5%$, and the accuracy of classification of the mean model was $69.6%{\sim}80.4%$. Among the models, the mean model of -one year, -two years, and -three years was highest in accuracy of classification (80.4%). Second, accuracy of prediction of final model adopted on validation samples showed 85% before one year of bankruptcy. The results of this study may be significant in that the results may be used as early warning system for bankruptcy prediction of KOSDAQ firms.

• International Area Studies Review
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• v.20 no.2
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• pp.63-83
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• 2016

This paper examines the determinants of efficiency in a KOSDAQ-listed manufacturing firm. We use Data Envelopment Analysis (DEA) to estimate the efficiency of the export manufacturing firm. We employ two inputs (number of employees, equity) and one output (sales) in the DEA. The determinants of export manufacturing firm efficiency are estimated using the panel Tobit model. An analysis of 369 export manufacturing firms from 2013 to 2015 indicates the following results: First, the R&D intensity, the wage and salary intensity, total asset, and equity ratio each had a negative impact on both the CCR and BCC efficiency scores. However, export intensity had a negative impact on CCR efficiency scores in a KOSDAQ-listed total export manufacturing firm. Second, the R&D intensity had a positive impact on both the CCR and BCC efficiency scores, but export intensity, the wage and salary intensity, and equity ratio each had a negative impact on the CCR and BCC efficiency scores in a KOSDAQ-listed large export manufacturing firm. Third, the R&D intensity, the wage and salary intensity, total asset, and equity ratio each had a negative impact on both the CCR and BCC efficiency scores; respectively, in a KOSDAQ-listed small and medium export manufacturing firm.

• 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.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.157-176
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• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.785-796
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• 2013

News Impact Curves(NIC) developed by Engle and Ng (1993) have been useful for graphically representing the volatilities arising from financial time series. Adding an improvement and refinement to the original NIC, this article proposes so called two dimensional NIC and principal component NIC. We illustrate the methodology via Kosdaq data.