• The Korean Journal of Financial Management
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• v.27 no.1
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• pp.121-156
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• 2010

Back-door-listing can be viewed both as M&A and an alternative to IPO. If IPO is an access to the capital market through regulations, back-door-listing would be the way of entering the market through trading. Back-door-listing can be a better choice considering the common wisdom that regulations hinder the functioning of free market system. One would, however, prefer IPO, for the informational asymmetry isless severe in case of IPO. This paper examines if IPO is superior to back-door-listing as to the informational efficiency. The excess buy-and-hold returns of the Kosdaq back-door-listing firms are estimated over the three-year-period since the event. They are compared against the excess buy-and-hold returns of the Kosdaq IPO firms over the same period of time. The results confirm this paper's prediction that IPO should be more information-efficient. Both IPO and back-door-listing firms start with high short-term excess returns and end up with long-term under-performance. However, back-door-listing firms show more significantly damaging long-term results. Furthermore, back-door-listing firms record poorer accounting results over the research period. These results imply that there exists fad at the time of both events and, in case of back-door-listing, this fad is reinforced by the possibility of window dressing.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.99-115
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• 2022

In this study, domestic academic journal papers on tax compliance, tax consciousness, and faithful tax payment (hereinafter referred to as "tax compliance") were comprehensively analyzed from an interdisciplinary perspective as a representative research topic in the field of tax science. To achieve the research purpose, topic modeling technique was applied as part of text mining. In the flow of data collection-keyword preprocessing-topic model analysis, potential research topics were presented from tax compliance related keywords registered by the researcher in a total of 347 papers. The results of this study can be summarized as follows. First, in the keyword analysis, keywords such as tax investigation, tax avoidance, and honest tax reporting system were included in the top 5 keywords based on simple term-frequency, and in the TF-IDF value considering the relative importance of keywords, they were also included in the top 5 keywords. On the other hand, the keyword, tax evasion, was included in the top keyword based on the TF-IDF value, whereas it was not highlighted in the simple term-frequency. Second, eight potential research topics were derived through topic modeling. The topics covered are (1) tax fairness and suppression of tax offenses, (2) the ideology of the tax law and the validity of tax policies, (3) the principle of substance over form and guarantee of tax receivables (4) tax compliance costs and tax administration services, (5) the tax returns self- assessment system and tax experts, (6) tax climate and strategic tax behavior, (7) multifaceted tax behavior and differential compliance intentions, (8) tax information system and tax resource management. The research comprehensively looked at the various perspectives on the tax compliance from an interdisciplinary perspective, thereby comprehensively grasping past research trends on tax compliance and suggesting the direction of future research.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.461-466
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• 2024

Korean local governments operates the participatory budgeting system autonomously. This study is to classify these entities into clusters. Among the diverse machine learning methodologies(Neural Network, Rule Induction(CN2), KNN, Decision Tree, Random Forest, Gradient Boosting, SVM, Naïve Bayes), the Support Vector Machine technique emerged as the most efficacious in the analysis of 2022 Korean municipalities data. The first cluster C1 is characterized by minimal committee activity but a substantial allocation of participatory budgeting; another cluster C3 comprises cities that exhibit a passive stance. The majority of cities falls into the final cluster C2 which is noted for its proactive engagement in. Overall, most Korean local government operates the participatory busgeting system in good shape. Only a small number of cities is less active in this system. We anticipate that analyzing time-series data from the past decade in follow-up studies will further enhance the reliability of classifying local government types regarding participatory budgeting.

• Management & Information Systems Review
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• v.37 no.4
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• pp.1-20
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• 2018

This study aimed to examine earnings quality of firms selected as Global Champs project which has been promoted by the government since 2013 to support small and medium sized enterprises, for the screening year(t-1) and selected year(t). Earing quality is measured as the value of discretionary accruals estimated by Dechow et al.(1995) adjusted Jones model and Kothari et al.(2005) model, respectively. I analyze the differences of earning quality between the Global Champ firms and the paired firms selected through criteria of the similar total assets and the same industry in the screening year and the selected year. This study is motivated by the needs of measurement of the performance of the Project from the accounting transparent point of view. As the results of this study, major findings are summarized as follows. Firstly the earnings quality of the selected firms was lower than that of the paired firms. This can be explained as a result of motivation of earnings management by companies eager to meet the requirements to be selected for the Project. Secondly, in the selected year, the earnings quality was proved to improve, comparing to the screening year. This can be explained by the efforts of companies to reinforce management innovation and transparent management, which in turn led to positive effects on the earnings quality. These findings were found to be consistent in the additional analyses, where the earning quality of the reconstructed sample with only selected companies was compared for the screening year and the selected year, based on the year before the screening year(t-2).

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• The Journal of Korean Academy of Prosthodontics
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• v.51 no.4
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• pp.292-299
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• 2013

Purpose: There is a growing concern for the cost management of medical institutions. The purpose of this study was to estimate Activity-Based Costing (ABC) for dental implant cost. ABC refers to allocating resources or cost based on the activities of services. Materials and methods: A dental institution located in the metropolitan area was selected in this study. The tax accounting data of the institution were utilized to confirm total cost, and the institution was asked to make out clinical activities to figure out what activities were carried out. The direct cost and indirect cost for dental implant were separately estimated, and cost driver was analyzed to estimate the indirect cost accurately. Results: The rates of the direct and indirect cost respectively stood at 35.8 and 49.5 percent. The cost for a dental implant was found to be approximately 1,579 won, and the cost of prosthetic surgery and treatment that included implant surgery accounted for the largest portion of the cost, which was 470 thousand won (30%). And the weight of training and education on dentistry was relatively higher than that of the other kinds of treatment. Conclusion: In order to ensure accurate and scientific costing for dental implant, not only direct medical procedure but every pre- and post-procedure activity should fully be taken into account. Pre-activities, post-activities, education and training are included in the indirect cost, but all these activities are mandatory and associated with the quality of treatment and the satisfaction level of patients.

• The Journal of the Korea Contents Association
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• v.17 no.4
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• pp.48-62
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• 2017

The present empirical research examines the effect of team leader's ethical leadership on team members' in-role behavior and psychological capital. This study also predicts that management decoupling and personal decoupling will moderate the effect of ethical leadership negatively. A growing body on leadership research highlights the role of team leader's moral manager in team settings. Ethical leadership also becomes a salient issue in the situation of unethical decision making and misuse of management power which have done by unethical leaders all around the world. In order to identify the effect of ethical leadership, I collected data of 922 team members from bank, semiconductor manufacturer, and university hospital. Our findings show that ethical leaderships have a positive effect on team members' in-role behavior and psychological capital. In addition, I also found the significant moderation effect of management decoupling which team members perceive their top management team's inauthenticity. Contrary to the prediction, however, the result doesn't support the moderation effect of personal decoupling. I discussed implications of confirming and disconfirming findings in details.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.174-182
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• 2018

Given the ongoing debate in many aspects of finance, more attention may need to focus on corporate R&D expenditures. This study empirically tests financial determinants of R&D expenditures for NYSE-listed and KOSPI-listed firms. Three major hypotheses were postulated to test for corporate R&D outlay. First, proposed variables such as one-year lagged R&D expenditures, market value based leverage, profitability and cash holdings showed significant influence on corporate R&D costs for the sample firms. Moreover, financial factors inclusive of squared one-year lagged R&D expenditures, the interaction effect between one-lagged R&D expenditures and high-growth firm, non-debt tax shield, Tobin's q and a dummy variable to explain differences in accounting treatment between the U.S. and Korea, revealed significant differences between the two samples. Finally, in the conditional quantile regression (CQR) analysis for the R&D-related variables in relation to corporate growth rate, it was found that the NYSE-listed firms had a statistically significant linkage between growth potential and one-year lagged R&D expenditures at lower quantile levels. This study may shed new light on identifying financial factors affecting differences between the U.S. market (as an advanced market) and the Korean market (as an emerging market) regarding the optimal level of R&D investments for shareholders.

• Korean Business Review
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• v.14
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• pp.31-44
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• 2001

The purpose of auditing is to express an auditor's opinion on the fair presentation of the financial position and business operations of companies according to the financial accounting standards, and to raise the reliability of the financial statements and to enable the user of the financial statements to make a proper judgement on the companies. There should be an audit risk in the audit of the financial statements in a modem sense because it is done by the sampling audit not by the detailed one. Audit risk is the risk that an auditor may unknowingly fail to modify appropriately the auditors' report on financial statements containing a material misstatement. The audit risk eventually hurt the reliability of the financial statements when the auditors set up different audit risks because it is determined by the auditor's professional judgement. Thus, there have been negative opinions on the Audit Risk Model suggested in the SAS No. 47 because it cannot explain the process of auditor's judgement and bring different results. In view of the results so far achieved, which influences the auditor's decision making, should be done by the Belief Function Mode Model in a position of raising the reliability of the financial statements and emphasizing the usefulness and effectiveness of the auditing.