• The Journal of Fisheries Business Administration
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• v.28 no.2
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• pp.1-18
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• 1997

The purpose of this study is to investigate empirically the characteristics of financial structure by using 76 fisheries corporations in Korea, and to suggest implications of the empirical results for government's financial policy for fisheries corporations. For the empirical test, we choose the following factors as the explanatory variables of cross-sectional regression analysis:firm-size(SIZE), collateral value of assets(TFATA), business risk(BRISK), growth(GROWTH), effective tax(ET), profitability(PROFIT). Two different debt ratios are used as dependent variables. One is defined as the ratio of total debt to total assets and the other is as that of long-term debt to total asset in terms of book value. The sample consists of 76 fisheries firms and sample period is 14 years from 1982 till 1995. From the results of cross-sectional regression analysis, the adjusted R$^2$values were high, 16∼79% and the overall F values indicated to be statistically significant. The results of cross sectional regression analysis show that the characteristics of financial structure fur fisheries corporations are as follows ; (1) Firm-size and collateral value of assets are the major factors of financial structure for fisheries corporations. That is, the larger firm-size the higher is debt ratio. This means that financial institutions conventionally lend more collateral loans with fixed assets like land, building rather than management capacities or credits. (2) To be consistent with a pecking-order theory, the higher is profitability the lower is debt ratio in fisheries corporations. (3) Corporations with high effective tax rate have lower financial leverage. Although the empirical results are inconsistent with traditional static trade-off theory, we think it would be attributed to government's various tax shelterings for fisheries which are likely to reduce tax shield effect of interests.

• The Journal of the Korea Contents Association
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• v.8 no.7
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• pp.224-232
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• 2008

Sport industry creates value-added by production and distribution of product or service related with sports or sports. Professional sports will lead to major area of sport service industry in the future. The purpose of study is to analyze financial condition and management performance by using financial statement(2005-2007) of korean professional soccer citizen teams. The analysis of financial condition and management performance is executed by financial ratio analysis method. The content of this study involve comparison with standardization ratio and financial ratios among professional soccer citizen teams. The results of this study are as follows. First, liquidity ratio measured by current ratio and quick ratio was high with compared to standardization ratio. Second, leverage ratio measured by debt ratio was very high. Third, activity ratio was good condition. Finally, profitability ratio was very low having minus ratio generally.

• Journal of KIISE:Databases
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• v.34 no.5
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• pp.379-388
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• 2007

Disordered streams may cause inaccurate or delayed results in window-based queries. Existing approaches usually leverage buffers to hand]e the streams. However, most of the approaches estimate the buffer size simply based on the maximum network delay in the streams, which tends to over-estimate the buffer size and result in high latency. In this paper, we propose a probabilistic approach to estimate the buffer size adaptively according to the fluctuated network delays. We first assume that intervals of tuple generations follow an exponential distribution and network delays have a normal distribution. Then, we derive an estimation function from the assumptions. The function takes a drop ratio as an input parameter, which denotes a percentage of tuple drops permissible during query execution. By describing the drop ratio in a query specification, users can control the quality of query results such as accuracy or latency according to application requirements. Our experimental results show that the proposed function has better adaptivity than the existing function based on the maximum network delay.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.11-20
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• 2016

This study examines the possibility of implementing the technology financing for export-import based small and medium sized enterprises. Our sample consists of 2,753 small and medium sized enterprises, receiving financial support from the Export-Import Bank of Korea for the period of 2011-2013. We find that only 400(200) firms reserve IPs(patents) annually. Given that IPs are likely to concentrate on manufacturer industries such as electronic components, computers, video, sound and communication equipment manufacturing(KSIC 26), other machinery and equipment manufacturing(KSIC 29), manufacture of motor vehicles and trailers(KSIC 31). We also find that the total assets, sales and R&D expenses of IP holding companies greatly exceeds those of companies without IPs. In addition, IP holding companies' liquidity seems slight edge and the leverage ratio is somewhat lower. However, profitability ratios of IP holding companies are rather than harsh or similar level. 20~30% of IP holding firms show very week credit scores, implying that banks' default risk is expected to be significant.

• Journal of Korea Trade
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• v.25 no.1
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• pp.65-88
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• 2021

Purpose - This paper aims to examine the determinants of SWFs' investment in Asian countries and to identify consistent investment patterns of SWFs in specific target firms from Asia, particularly China and South Korea. Design/methodology - This study extends the Tobin's Q model to examine the relationship between SWF investments in target firms and their returns with other firm-level control variables. We collect consistent data on SWF investments and the matched firm-level data on target firms, which of observation is 1,512 firms (333 in South Korea and 1,179 in China) targeted by 20 SWF sources during 1997-2017. The panel random effect model is used to estimate the extended Tobin's Q model. The robustness of the estimations is tested by the simultaneous equation models and the panel GEE model. Findings - The evidence shows that sovereign wealth funds are more inclined to invest in the financial sector with a monopoly position and in large firms with higher growth opportunity and superior cash asset ratios in China. In contrast to their investments in China, sovereign wealth funds in South Korea prefer to invest in strategic sectors, such as energy and information technology, and in large firms with high performance and low leverage. Sovereign wealth funds' investments tend to significantly improve the target firm's performance measured by sales growth and returns in both Korea and China. Originality/value - The existing literature focuses on examining the determination of SWFs investment in the developed countries, such as Europe and the United States. Our paper contributes to the literature in three ways; first, we analyzes case studies of SWF investments in Asian markets, which are less developed and riskier. Second, we examine whether the determination of SWF investment in Asian target firms depends on the different time periods, on types of sources of SWFs, and on acquiring countries. Third, our research uses vast sample data on target firms in longer time periods (1997-2017) than other previous studies on the SWFs for Asian markets.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.659-666
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• 2023

Characterized by boom-and-bust cycles, low entry barriers, and an almost perfectly competitive structure, the construction industry presents a unique challenge for the survival and growth of its constituent companies. A crucial aspect of this challenge is the ongoing monitoring of their financial health and business performance. To understand the typical financial and operational status of construction companies, this study analyzes the financial statements of 6,252 such companies, all of which have undergone at least one external audit between 2000 and 2019. These statements were used to develop combined financial profiles and derive industry averages. The findings indicate that the construction industry experiences limited growth in sales and profitability. High leverage ratios can jeopardize financial stability, pushing companies to seek production efficiency, such as enhancing gross asset turnover. This tendency has been particularly noticeable in the aftermath of the global financial crisis in 2008.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.110-118
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• 2017

This study investigated empirically one of the controversial subjects in modern finance, in that there is an optimal level of capital structure for KOSPI-listed firms in the Korean capital market. Given the major theories on the capital structure, such as Myers' pecking order, trade-off, and agency cost ones, this study applied an analysis of covariance models in parametric and non-parametric statistical methods. In particular, two covariates to control for the possible effects of trade-off and agency cost, were employed separately in each corresponding model, while the other proxy for pecking order rationale was adopted in previous research [1] to conduct inter- and intra-industry analyses. Based on the outcomes obtained from the study, it was demonstrated empirically that there are optimal capital structures for firms in the sample industries at the inter-industry level, whereas statistical differences indicating non-existence of an optimal point, were revealed within the industry. Accordingly, these findings suggest a new vision to potential investors that firms in the domestic market may have financial opportunities to increase their value by gradually adjusting the leverage ratios in terms of the intra-industry perspective.

• Journal of the Korean Society for Library and Information Science
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• v.44 no.4
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• pp.231-247
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• 2010

This research examines a number of theoretical and practical issues when measuring the economic value of library services. In particular, using two recent studies conducted in Korea as illustrations, the study shows how various measurement decisions affect the final outcomes in the economic valuation of library services and thus points to the need for a more reliable study design. Specific areas of measurement discussed include the following: scope of measurement, application of CVM(Contingent Valuation Method), time vs. monetary value measurement, dealing with outliers, allowing alternatives, and the use of estimation. ROI(Return on Investment) scores or benefit cost ratios vary significantly according to different measurement choices even in the same study. There is a need for collecting qualitative data that complements the quantitative data typically collected in economic valuation studies. The outcome of economic valuation of library services should be considered as one of many representations of library values. Practitioners and researchers should exercise caution in interpreting those results but be able to leverage them to better communicate the value of library services.

• Journal of Navigation and Port Research
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• v.45 no.3
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• pp.173-180
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• 2021

Although accounting is one of the core fields of corporate management, few studies have reported accounting phenomena involving shipping companies. In addition, although financial reporting is very important to shipping companies that use several financial tools such as ship finance and financial lease, it is difficult to identify studies investigating shipping companies' financial reporting, especially their earnings management. The purpose of this study is to analyze accrual earnings management behavior of shipping companies. Companies with high debt ratios and net losses are known to have incentives for earnings management. Due to the nature of the industry, shipping companies have a high debt ratio and often report net losses. Accordingly, shipping companies are expected to engage in substantial earnings management. Based on the analysis of KOSP I companies listed on the Korea Exchange from 2001 to 2020, it was found that shipping companies are engaged in higher levels of earnings management than non-shipping companies. Discretionary accrual was used as a proxy variable for earnings management. Discretionary accrual was measured using the modified Jones model of Dechow et al. (1995) and the performance matched model of Kothari et al.(2005). In this study, significant results were derived by comparatively analyzing the earnings management practices, which is one of the major accounting behaviors of shipping and non-shipping companies. Stakeholders such as external auditors, investors, financial institutions, analysts, and government authorities need to be aware of the earnings management behavior of listed shipping companies during their external audit, financial analysis, and supervision. Finally, listed shipping companies must conduct stricter accounting based on accounting principles.

• 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.