• Korean Journal of Construction Engineering and Management
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• v.5 no.4 s.20
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• pp.97-106
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• 2004

Credit evaluation by domestic financial institutions on Korean construction companies has had many problems with its tools and criteria ignoring the industrial characteristics. This study develops the DCiF(discounted cash inflow) model as a solution and discusses its usage in construction financing. It also examines the significance of the DCiF indices through regressions and statistical comparison with the other credit evaluation estimates. The results show its clear significance and consistent fitness. Based on the empirical results, implications and methodology are provided for the effective use of the indices in credit evaluation on the construction companies.

• Korean Journal of Construction Engineering and Management
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• v.21 no.5
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• pp.64-73
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• 2020

The government announced a plan for fund support to the enterprises with high possibility of recovery and early restructuring for the enterprises with low recovery by objectifying credit assessment system. Such announcement of government could be extended to restructuring risk of middle standing enterprises with low financial soundness by establishing the basis to prepare prompt restructuring by reinforcing the basis for restructuring through capital market. This research analyzed financial soundness based on the financial evaluation of bank by selecting 10 middle standing construction companies which focused on housing business in 2019, based on such analysis result, it was confirmed that there was a high possibility of restructuring risk. This research determined that there would be a decrease in growth rate of construction industry on the whole in 2020 due to fall of economic growth rate and reinforced real estate regulation, accordingly, there's a big possibility for middle standing construction companies with paid-in capital ratio due to its low possibility of maintenance of stable credit rating. This research established KCSI assessment model by utilizing the material of a reliable research institute in order for middle standing construction companies to evade restructuring risk, and indicated risk ratio differentiated per each item through a working-level expert survey. Such research result could suggest credit risk reduction method to middle standing construction company management staffs, and prepare a basis to evade restructuring risk.

• Korean Journal of Construction Engineering and Management
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• v.13 no.6
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• pp.107-120
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• 2012

This study is conducted so as to understand actual condition of financial difficulties confronted by construction companies in the recession of real estate market which has been continued since the financial crisis started from USA in the second half of 2008, and provide fundamental data for the establishment of policy direction. Compared with this actual condition survey with a 2008 investigation, it seems that the practice of financial institutions or credit evaluation relating parts among sections, which were pointed as problems in such investigation, are resolved to some extent. It seems that there are many causes to aggravate financial conditions as pointed at this time and such causes are related to self-problems, which are inherent to the construction business, such as the smooth settlement of construction payment, the securement of new construction projects, the limitation according to the risk inherent to the construction business, and the industry vision, etc.

• Korean Journal of Construction Engineering and Management
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• v.12 no.3
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• pp.53-61
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• 2011

After several collapse accidents of large structures in the early 1990s in Korea, the government enacted a law that architectural, engineering and construction firms are obliged to have insurance for projects over a certain size. Particularly, with regard to insurance in design and construction supervision works (i.e. engineering insurance), although several operation-based problems were pointed out from practitioners, still little research has been done on analyzing current regnlartory and operational state and suggesting policy alternatives. Hence, this study applies Delphi technique to solicit current operational problems and propose a series of improvements on engineering insurance based on interview surveys targeting major market participants: municipalities, engineering firms, and insurance companies. Key findings culminate in adopting guarantee limits based on credit evaluation, abrogating joint surety, covering a loss of life, increasing insurance entrance fee, extending time covered, and etc. Reaching a consensus on the proposed alternatives between the market participants will form the foundation for sound developments of construction design and engineering industry.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.