• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.436-448
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• 2015

This study examined one of the contemporary financial aspects, the level of corporate cash holdings for the firms belonging to the chaebols in the Korean capital markets. Being accompanied by various alternative econometric methodologies such as static and dynamic panel data model, stepwise OLS, and Fama-Macbeth modelm this research extended the preceding Kim's study (2015) in anticipation of validating the results to identify any financial factors which may significantly affect the chaebol firms' cash reserves. Several financial characteristics such as CASHFLOW, MVBV, REINVEST, and AGENCY, were found to be statistically significant factors on the level corporate liquidity, along with CCC as cash conversion cycle in the models. It may be plausible that any outcomes of this study may be applied to enhance the efficiency of financial strategies of the chaebol firms on cash holdings, thereby expediting the development of the domestic capital markets status quo toward the advanced one in the market classification.

• Korean Management Science Review
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• v.32 no.1
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• pp.113-131
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• 2015

We examine the relationship between firms' environmental (E), social (S), and governance (G) factors, with their financial performance in order to provide an empirical rationale for CSV (creating shared value) pursuing both of firms' profitability and CSR (corporate social responsibility). The financial performance is classified into four aspects such as profitability, stability, efficiency, and cash-flow, and each of these aspects is measured by two financial ratios respectively. To measure the firms' ESG performance, we employ the published performance grades by the Korea Corporate Governance Service for a three year span, from 2011 to 2013. Total of eight regression analyses are performed. The results show that firms' non-financial performance in general has statistically significant positive relationships with return on assets, return on net sales, and cash-flow from operating activities ratio, while it has negative relationships with net working capital ratio, asset turnover ratio, and cash-flow from investing activities ratio. It has no significant relationships with debt ratio and equity turnover ratio. The results imply that firms' non-financial performance may have a negative impact on some financial performance such as liquidity and efficiency in a short term, but it would eventually improve the firms' profitability and cash-generating ability, which provides an empirical evidence for the concept of CSV, and motivates the firms to participate in social contribution activities without sacrificing their profitability for their respective sustainablity management.

• The Journal of the Korea Contents Association
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• v.17 no.6
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• pp.504-513
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• 2017

This study investigated one of the controversial issues on debate or even controversial between policy makers at the government and corporate levels: To examine any financial determinants on the cash holdings of the firms in the advanced and emerging capital markets. Futhermore, it focused on the large representative firms headquartered in the U.S. and the Republic of Korea, taking into account scarcity of the previous literature concentrated on the comparative studies on this particular subject. Several legitimate, but robust econometric estimations such as static and dynamic panel data models and Tobit regression, were applied to investigate possible financial factors ono the cash liquidity. Given the continued debates or arguments on the excess cash reserves between interest partied at the government and corporate levels in the advanced and/or emerging capital markets, and more accelerated capital transfers among associated nations by engaging in the arrangements of the FTAs, the results of the study may provide a vision to search for the optimal level of corporate cash holdings for firms in the two nations.

• Asia pacific journal of information systems
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• v.26 no.1
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• pp.41-65
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• 2016

What prevails in the today's research on social media is a functional view of technology. Technology is regarded as a set of technical devices used to conduct specific social functions, such as personal communication, social networking, public posting, and corporate advertising, among others. This paper proposes that such a functional view of technology renders social media research unduly limited and constrained in its scope, level, and direction of inquiry. Problematizing on some representative social media research efforts in the field of IS, this paper provides an alternative perspective, that is, to view social media as a technology-for-being that exerts a deeper level of influence on our existence, molding and shaping the nature and mode of being itself. Such a technology-for-being perspective has been rarely explored or subscribed to in the present IS social media research. Building upon the new conception of social media as a technology-for-being, this essay explores the quality of being in the context of social media. Five such qualities are discussed, including virtuality, materiality, externality, liquidity, and hybridity. The essay also explores the deep structural problems of research to guide future social media research. Six of such problems include Problematize-the-Natural, Follow-the-Actor, Welcome-the-Frankenstein, Weber-meets-Frankenstein, Freud-meets-Frankenstein, and Marx-meets-Frankenstein. The essay concludes with discussions on the implications of the essay, its limitations, and suggestions for future work.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.141-148
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• 2020

This paper analyzes factors affecting the debt maturity structure of enterprises listed on the Vietnam stock market. The panel data of research sample includes 549 non-financial listed enterprises on the Vietnam stock market from 2009 to 2019. The Generalized Least Square (GLS) tool is employed to address econometric issues and to improve the accuracy of the regression coefficients. In this research, debt maturity structure is the dependent variable. Capital structures, fixed assets, liquidity, firm size, asset maturity, profitability, corporate income tax, gross domestic product, inflation rate, credit growth scale are independent variables in the study. The model results show, that among the factors affecting the structure of debt maturity, the capital structure, asset structure, and firm size have the highest estimation coefficients, which shows that capital structure, asset structure, and firm size plays an important role in the decision-making process of debt maturity structure. The empirical results show that there are differences in the impact of these factors on the debt maturity structures in state-owned enterprises and non-state enterprises listed on the Vietnam stock market. The findings of this article are useful for business administrators, helping business managers make the right financial decisions to determine the target debt maturity structure in enterprises.

• 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.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.729-739
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• 2016

The primary objective of implementing the study was to further investigate any pronounced financial components affecting the level of cash retention for the Korean chaebol firms. The research was framed to test for two hypotheses on the cash savings with utilizing the chaebol firms during the post-era of the global financial turmoil (from 2009 to 2013). In the first hypothesis test, any significant explanatory variables relative to the cash holdings, were identified in each corresponding category of the conditional quantile regression (CQR) model, while multilogistic regression analysis was performed to discriminate relevant financial factors in each pair of classes consisting of the chaebol firms. Concerning the results, liquidity, agency costs, and cash conversion cycle were found to be statistically significant in the majority of classified categories in the former test and liquidy, firm size, and dividend yield, also showed discriminating powers in each pair of categorical for the firms in the latter test.

• Journal of Navigation and Port Research
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• v.37 no.2
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• pp.181-186
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• 2013

In a corporate financing, the decision of optimal capital structure is becoming more critical issues and still remaining a problem to be solved though many of researcher have studied. Particularly, shipping companies need a huge amount of capital finance for new vessel's capacity and then they are considering what is the best capital structure. In this point of view, this study tries to investigate the determinants of debit maturity structure focused on the Korean shipping industry. As results of panel regression analysis, firm size, liquidity, chance of growth, good cash flow are major determinants of debit expiration structure in the Korean shipping companies.

• Asia-Pacific Journal of Business
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• v.10 no.4
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• pp.205-221
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• 2019

The purpose of this research is to investigate the effect of cash holdings on firm value in export companies. To investigate this effect, we analyzed 5,386 samples drawn from export companies listed in the KOSDAQ from 2011 to 2018. During this period, the International Financial reporting Standards have been employed. The research results are as follows. First, the results of a T-test showed that the level of the firm value of export companies with high levels of cash holdings is significantly higher than that of those with low levels of cash holdings. In addition, the level of the firm value of export companies with higher levels of cash holdings than in the previous year is higher than the level might otherwise be. Furthermore, the effects of cash holdings on firm value are similar to those on return on asset. These results suggested that export companies have little used a way of increasing their debt levels in order to increase cash holdings. Second, the results of a multivariate regression analysis presented that the cash holdings of export companies in listed the KOSDAQ significantly influence their firm value. Moreover, a higher level of cash holdings than in the previous year significantly affect firm value. These results proposed that making higher cash holdings than in the previous year might be useful in enhancing firm value. We found that export companies efforts to increase cash holdings positively influence changes in firm value. We also found that Korean export companies maintain their financial stability by obtaining sufficient liquidity specifically in a high uncertainty era like the recent time. We finally firmed an effort to maintain cash holdings as a reasonable choice for export companies.