• Title/Summary/Keyword: Insolvent Company

Search Result 8, Processing Time 0.023 seconds

An Analysis on the Investment Determinants for Insolvent Housing Development Projects (건설회사의 공동주택 PF 부실사업장에 대한 투자결정요인 분석)

  • An, Kukjin;Cho, Yongkyung;Lee, Sangyoub
    • Korean Journal of Construction Engineering and Management
    • /
    • v.15 no.2
    • /
    • pp.112-121
    • /
    • 2014
  • After IMF bailout crisis in Korea, project financing has been employed as a major funding vehicle for the housing development. In 2008, the recession of housing market due to the global financial crisis had an significant impact on the increasing insolvent site of PF based housing development project, resulting in serious impact to whole economy as a chain effect. In order to resolve this vicious circle of bankruptcy, the major construction companies were urged to take over the insolvent sites and invest to them for normal project exit, and finally play a critical role in normalization of market. Therefore, this study aims to define the core factors for decision making to invest to insolvent site and find out differences among constructors, developers, financial lenders. The results from AHP analysis, the profitability was the most important factor to constructors. Moreover, even though the location merit is little less, through competitive price, we can assure that stable profitability is most important factor to decide to invest in insolvent site. In conclusion, the price is cheap, is highly feasible, if the land secured, major construction company will participate in a PF business investment. These findings were verified by the investment case of major construction company.

Comparative Analysis of Default Risk of Construction Company during Macroeconomic Fluctuations (거시경제변동 전후 건설기업의 부실화 비교분석 - IMF 외환위기 및 서브프라임 금융위기 전후를 중심으로 -)

  • Choi, Jae-Kyu;Yoo, Seung-Kyu;Kim, Jae-Jun
    • Korean Journal of Construction Engineering and Management
    • /
    • v.13 no.4
    • /
    • pp.60-68
    • /
    • 2012
  • The past IMF foreign exchange crisis and subprime financial crisis had a big influence on variability of macroeconomics, even if the origin of its occurrence might be different. This not only had a significant infrequence on the overall industries, but also produced many insolvent companies by being closely linked with a management environment of an individual construction company leading the construction industry. Actually, the level of default risk of construction companies before and after fluctuation of macroeconomics gets to experience a rapid changing process, and a difference in reaction against shock exists according to each company. Accordingly, the purpose of this paper is to confirm the fluctuation process of the default risk of construction companies under the fluctuation of macroeconomics such as the IMF financial crisis and the subprime mortgage crisis. As an analysis result, it is judged that the subprime financial crisis gave bigger shock to construction companies than the foreign exchange crisis, and it is expected that this would have a relation with the construction market before shock of macroeconomics. In addition, it was analyzed that when comparing insolvent companies with normal companies, the recovery speed of normal companies is faster. It is judged that this was affected by a difference of internal business capacity between insolvent companies and normal companies.

A Study on the Insolvency Prediction Model for Korean Shipping Companies

  • Myoung-Hee Kim
    • Journal of Navigation and Port Research
    • /
    • v.48 no.2
    • /
    • pp.109-115
    • /
    • 2024
  • To develop a shipping company insolvency prediction model, we sampled shipping companies that closed between 2005 and 2023. In addition, a closed company and a normal company with similar asset size were selected as a paired sample. For this study, data of a total of 82 companies, including 42 closed companies and 42 general companies, were obtained. These data were randomly divided into a training set (2/3 of data) and a testing set (1/3 of data). Training data were used to develop the model while test data were used to measure the accuracy of the model. In this study, a prediction model for Korean shipping insolvency was developed using financial ratio variables frequently used in previous studies. First, using the LASSO technique, main variables out of 24 independent variables were reduced to 9. Next, we set insolvent companies to 1 and normal companies to 0 and fitted logistic regression, LDA and QDA model. As a result, the accuracy of the prediction model was 82.14% for the QDA model, 78.57% for the logistic regression model, and 75.00% for the LDA model. In addition, variables 'Current ratio', 'Interest expenses to sales', 'Total assets turnover', and 'Operating income to sales' were analyzed as major variables affecting corporate insolvency.

Analyzing on the Fluctuation Characteristics of Management Condition of Construction Company (건설업체 경영상태 변동에 대한 특성 분석)

  • Jang, Ho-Myun
    • Journal of the Korea Academia-Industrial cooperation Society
    • /
    • v.15 no.2
    • /
    • pp.1118-1125
    • /
    • 2014
  • The past IMF foreign exchange crisis and subprime financial crisis had a big influence on variability of macroeconomics, even if the origin of its occurrence might be different. This not only had a significant infrequence on the overall industries, but also produced many insolvent companies by being closely linked with a management environment of an individual construction company leading the construction industry. The purpose of this research is to investigate characteristics of management condition of construction company according to the size of construction company using KMV model developed on the basis of the Black & Scholes option pricing theory. This research has set 28 construction companies listed to KOSPI/KOSDAQ for applying the KMV model and measuring the level of the default risk of construction companies. The data was retrieved from TS2000 established by Korea Listed Companies Association (KLCA), Statistics Korea. The analysis period is between first quarter of 2004 and fourth quarter of 2010. This research examine characteristics of the level and fluctuation process of the management condition of construction company according to the size of construction company.

A Study on Financial Ratios Change of Korean Dry Bulk Shipping Firms before and after the 2008 Global Financial Crisis (글로벌 금융위기 전후 한국 건화물 선사의 재무비율 변동에 대한 비교 분석)

  • Cho, In-Seong;Ryoo, Dong-Keun;Lee, Ki-Hwan
    • Journal of Navigation and Port Research
    • /
    • v.44 no.3
    • /
    • pp.244-252
    • /
    • 2020
  • The 2008 global financial crisis was triggered by the Lehman Brothers crisis caused by the sub-prime mortgage crisis in the United States This crisis has had an impact on the globe's dry bulk shipping market by reducing dry bulk cargo volume. An oversupply of dry bulk carriers caused a serious recession in the globe's dry-bulk shipping industry and shipbuilding industry. In this situation, the Korean dry-bulk shipping companies were victims of the quagmire of a long recession since the global financial crisis and could not overcome this crisis. This condition forced them into severe financial risk Thus, it caused many shipping companies to file for bankruptcy. In this study, we classified Korean ocean-going dry-bulk shipping companies into two groups, that is, the solvent group and the insolvent group. We also separated the research period before and after the 2008 global financial crisis. Then we investigated the differences in the major financial ratios of the two groups by t-test and found that some financial ratios such as profitability ratios and growth ratios showed the difference between the two groups with statistical significance. The significance of this study is as follow. First, the shipping company management is also crucial for the systematic management of financial strength and business strategy, it is crucial to manage cargo which a high profitable freight. Second, the shipping company should be managed as a company with continued growth through efficient operation and management of ships.

An Empirical Study on Bankruptcy Factors of Small and Medium-sized Venture Companies using Non-financial Information: Focusing on KCGF's Guarantee-linked Investment Companies (비재무정보를 이용한 중소벤처기업의 부실요인에 관한 실증연구: 신용보증기금의 보증연계투자기업을 중심으로)

  • Jae-Joon Jang;Cheol-Gyu Lee
    • Journal of Industrial Convergence
    • /
    • v.21 no.6
    • /
    • pp.1-11
    • /
    • 2023
  • The purpose of this study is to verify the factors affecting corporate bankruptcy by using non-financial information of companies invested by credit guarantee institutions. In this study, 594 companies (525 normal companies, 69 insolvent companies) invested in by the Korea Credit Guarantee Fund from March 2014 to the end of December 2022 were selected as samples. Non-financial information of companies was divided into founder characteristics information, company characteristics information, and corporate investment information, and cross-analysis and logistic regression analysis were conducted. As a result of the cross-analysis, personal credit rating, industry, and joint investment were selected as significant variables, and logistic regression analysis was conducted for those variables, and two variables, personal credit rating and joint investment, were selected as important factors for bankruptcy. In business management, the founder's personal credit and the importance of joint investment in investment support were found out. It will help to minimize bankruptcy if institutions that support investment in SMEs reflect these results in their screening and systematically build cooperative relationships with private investment institutions. It is hoped that this study will provide an opportunity to pay more attention to the factors that affect the bankruptcy of companies that receive direct investment from public institutions.

Analysis on the Labor Market Performance of Local University Graduates and Regional Education Gap (지방대학 졸업자의 노동시장 성과와 지역별 교육격차)

  • Kim, Hisam
    • KDI Journal of Economic Policy
    • /
    • v.32 no.2
    • /
    • pp.55-92
    • /
    • 2010
  • In terms of labor market accomplishments, such as income, size of the company, and the matching quality between one's job and college major (specialization), a very large discrepancy is observed between the graduates from colleges located in Seoul and those outside Seoul. But, when the department average score of the Scholastic Aptitude Test (SAT) at the time of college entrance is controlled for, the discrepancy is found to be reduced to a considerable degree. In the case of wage gap, at least two third can be explained by the SAT score gap. The remaining wage gap seems to reflect the characteristics of workplace. In other words, graduates with high SAT scores enter colleges located in Seoul and thus tend to find better jobs leading to earning differences. This result that confirms the importance of aptitude test scores suggests that in the labor market, one of the major reasons behind a lower accomplishment of the graduate from local colleges is due to a lower competitiveness of local colleges in attracting the brightest students. But, this should not be viewed as only an internal problem of local colleges. This is because the growth of local economies tends to haul the advancement of local colleges in that area rather than being the other way around. The agglomeration effect in Seoul where headquarters of large corporations and financial institutions gather is the factor that has elevated the status of colleges located in Seoul since this provides highly preferred job choices of graduates. When the competitiveness of college is significantly influenced by exogenous factors, such as the vicinity to Seoul, the effort being made by colleges alone would not be enough to improve the situation. However, the central government, too, is not in the position to carry out countermeasure policies for such problems. The regional development strategy boosted through supportive policies for local colleges, such as financial support, is not based on the persuasive and empirical grounds. It is true that college education is universal and that the government''s intervention in assisting local colleges to secure basic conditions, such as tenure faculty and adequate facilities is necessary. However, the way of intervention should not be a support-only type. In order to improve the efficiency and effect of financial support, restructuring programs, including the merger and integration of insolvent colleges, should be underway prior to providing support. In addition, when the policy is focused on education recipients-local college students, and not on education providers-local colleges, the importance of regional gap in compulsory education (elementary and junior high schools) turns out to be much important as the gap between metropolitan area colleges and local colleges. Considering the educational gap before college entrance shown from the discrepancies of aptitude test scores among different regions, the imbalance between regions in terms of human resources is apparently derived from compulsory education, and not from college education. Therefore, there is a need to double the policy efforts to reduce the educational gap among different regions. In addition, given the current situation where it is difficult to find appropriate ex post facto policy measures to solve the problem of income gap between the graduates from metropolitan colleges and local colleges, it can be said that improving the environment for compulsory education in local areas is a growing necessity for bridging the educational gap among different regions.

  • PDF

The Prediction of Export Credit Guarantee Accident using Machine Learning (기계학습을 이용한 수출신용보증 사고예측)

  • Cho, Jaeyoung;Joo, Jihwan;Han, Ingoo
    • Journal of Intelligence and Information Systems
    • /
    • v.27 no.1
    • /
    • pp.83-102
    • /
    • 2021
  • The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.