• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.147-156
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• 2016

This study analyzed the impact of changes in the construction business on construction company insolvency according to their size using the vector error correction model. First, this study applied EDF (Expected Default Frequency), which was calculated by KMV (Kealhofer, McQuown and Vasicek) model, as a variable to indicate the insolvency of construction companies. This study set 30 construction companies listed to KOSPI/KOSDAQ for estimating the EDF by size and construction companies were divided into two groups according to their size. To examine the construction business cycles, the amount of construction orders according to the type-residential, non-residential, and civil work- was used as a variable. The serial data was retrieved from TS2000 established by the Korea Listed Companies Association (KLCA), Statistics Korea. The analysis period was between the second quarter of 2001 and fourth quarter of 2015. As a result of calculating the EDF of construction companies by size, as it is generally known, the large-sized construction companies showed lower levels of insolvency than relatively smaller-sized construction companies. On the other hand, impulse response analysis based on VECM confirmed that the level of insolvency of large-scaled companies is more sensitive to business fluctuations than relatively smaller-sized construction companies, particularly changes in the residential construction market. Hence it is a major factor affecting the changes in insolvency of large-sized construction companies.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.13-21
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• 2016

Insolvency of construction companies that play the role of main contractors can lead to clients' losses due to non-fulfillment of construction contracts, and it can have negative effects on the financial soundness of construction companies and suppliers. The construction industry has the cash flow financial characteristic of receiving a project and getting payment based on the progress of the construction. As such, insolvency during project progress can lead to financial losses, which is why the prediction of construction companies is so important. The prediction of insolvency of Korean construction companies are often made through the KMV model from the KMV (Kealhofer McQuown and Vasicek) Company developed in the U.S. during the early 90s, but this model is insufficient in predicting construction companies because it was developed based on credit risk assessment of general companies and banks. In addition, the predictive performance of KMV value's insolvency probability is continuously being questioned due to lack of number of analyzed companies and data. Therefore, in order to resolve such issues, the Bayesian Probabilistic Approach is to be combined with the existing insolvency predictive probability model. This is because if the Prior Probability of Bayesian statistics can be appropriately predicted, reliable Posterior Probability can be predicted through ensured conditionality on the evidence despite the lack of data. Thus, this study is to measure the Expected Default Frequency (EDF) by utilizing the Bayesian Probabilistic Approach with the existing insolvency predictive probability model and predict the accuracy by comparing the result with the EDF of the existing model.

• Journal of Technology Innovation
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• v.25 no.1
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• pp.35-60
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• 2017

In this study, the effects of Venture Start-up's Technological Capabilities on Financial Stability and Possibility of Insolvency was investigated by use of SEM(Structural Equation Model). Technological Business Capabilities include CEO's Technological Capability, Management Specialization and the Feasibility of the Investment plan. The empirical data for this study were taken from the technology assessment data of Korea Technology Guarantee Fund(KTGF) on 1,419 Venture Start-ups from 2011 until 2012 and the financial data of the following 2 years of the sample. Venture Start-ups established within 7 years, were selected for this study's sample from viewpoint of their 'High-Risk High-Return' characteristic. The results are as follows : Manpower including CEO's Technology-related Knowledge and Experience, Management Organization's Technological Specialization and Cooperativeness, Reasonable Investment and Financing Planning etc. were proved to improve Financial Stability, and therefore reduce Possibility of Insolvency.

• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.1
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• pp.61-70
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• 2012

A construction project is very costly and takes a long time to make investment and yield profit. For this reason, financial institutions are cautious about financing construction projects. Meanwhile, a construction company needs financing from financial institutions to cover a large expense of a construction project. Thus, there is likely to be a close correlation between financing conditions and business operation of a construction company. To examine the relationship, variables were identified that are related to insolvency of a construction company and changes in financing conditions. The analysis period is between the second quarter of 2001 and the fourth quarter of 2010. Data was retrieved from TS2000 established by Korea Listed Companies Association (KLCA), Statistics Office, and Construction Economy Research Institute of Korea (CERIK). In terms of methodology, VECM (Vector Error Correction Model) was used to analyze dynamic relationship between changes in financing conditions and insolvency of a construction company based on the identified variables. The hypothesis was that changes in financing conditions would significantly affect business of a construction company, but, the analysis did not find a close relation between the two factors. However, it was shown that poor business of a construction company affects financing conditions adversely.

• Journal of Family Resource Management and Policy Review
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• v.18 no.4
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• pp.27-52
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• 2014

This study aims to investigate the financial soundness of Korean households and its effects on the retirement preparation of these households. The sample consisted of 1,031 households selected from the 4th Korean Retirement and Income Study (KReIS) by the National Pension Research Institute in 2012. The empirical results are as follows. According to the logistic regression model, the statistically significant factors affecting the retirement preparation of Korean households are gender, occupation type, residence, satisfaction with economic condition, and type of financial soundness-sound households or insolvency-risky households. In other words, more female-headed households and households with higher levels of occupation are less likely to prepare for retirement. The households that are more likely to prepare for retirement are those that are lived in metropolitan areas as opposed to the countryside; further, households that are more economically sound are also more likely to prepare for retirement. In particular, sound households and insolvency-risky households are less likely to prepare for retirement compared to liquidity-risky households.

• Journal of Arbitration Studies
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• v.6 no.1
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• pp.407-431
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• 1996

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.43
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• pp.275-295
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• 2009

After a long period of development and worldwide consultation, the London-based Joint Cargo Committee has revised the Institute Cargo Clauses (A), (B) & (C) and some ancillary Institute Clauses. The revision mainly include a clarification of the exclusions within the clauses, some modernization of the language of the clauses and new definitions of some terms. With these revisions, the coverage is widened to offer more protection to the assured. This may enable the widely used Institute Cargo Clauses to receive even greater worldwide acceptance. The following are the main changes in the new 2009 ICC compared with the 1982 ICC. 1. Insufficient or unsuitable Packing or Preparation(Clause 4.3): The revised clause is more favourable to the assured because under the revised clause this sub-clause is only applicable to (a) where packing or preparation is carried out by the assured or their employees or (b) packing or preparation takes place before the attachment of the risk. 2. Insolvency or Financial Default (Clause 4.6): The insolvency and financial default wording is incorporated in the revised clauses, making it more favourable to the assured. 3. Unseaworthiness (Clause 5): The revision is more favourable to the assured in that it limits the exclusion in relation to the unfitness of vehicles, vessels or containers to cases where the assured or their employees are privy to such unfitness. 4. Terrorism (Clause 7): A new definition of "terrorism" is introduced and the revised clause also widens the acts of an individual to encompass ideological and religious motives.

• Journal of Korea Technology Innovation Society
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• v.10 no.2
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• pp.183-205
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• 2007

An investigation was undertaken of the optimal discriminant model for predicting the likelihood of insolvency in advance for medium-sized firms based on the technology evaluation. The explanatory variables included in the discriminant model were selected by both factor analysis and discriminant analysis using stepwise selection method. Five explanatory variables were selected in factor analysis in terms of explanatory ratio and communality. Six explanatory variables were selected in stepwise discriminant analysis. The effectiveness of linear discriminant model and logistic discriminant model were assessed by the criteria of the critical probability and correct classification rate. Result showed that both model had similar correct classification rate and the linear discriminant model was preferred to the logistic discriminant model in terms of criteria of the critical probability In case of the linear discriminant model with critical probability of 0.5, the total-group correct classification rate was 70.4% and correct classification rates of insolvent and solvent groups were 73.4% and 69.5% respectively. Correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify the present sample. However, the actual correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify a future observation. Unfortunately, the correct classification rate underestimates the actual correct classification rate because the data set used to estimate the discriminant function is also used to evaluate them. The cross-validation method were used to estimate the bias of the correct classification rate. According to the results the estimated bias were 2.9% and the predicted actual correct classification rate was 67.5%. And a threshold value is set to establish an in-doubt category. Results of linear discriminant model can be applied for the technology financing banks to evaluate the possibility of insolvency and give the ranking of the firms applied.

• Korean Journal of Construction Engineering and Management
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• v.14 no.3
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• pp.3-11
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• 2013

The changes in construction business have impact on overall operation of construction companies. Poor business of construction companies following a s low industrial cycle could have broader implications and influences on the industry. Since a construction project involves various stakeholders including public organizations, financial institutions and households, a downturn in construction industry might lead to significant economic loss. In this regard, it is meaningful to examine the relationship between changes in construction business cycles and insolvency of construction companies. This study conducts an empirical analysis of the relationship between construction business cycles and how much they affect operation of construction companies. To this end, KMV model was used to estimate probability of bankruptcy, which represents business condition of a construction company. To examine construction business cycles, investment amount for different construction types-residential, non-residential, and construction work-was used as a variable. Based on the investment amount, VECM was applied and the analysis results suggested that construction companies should put priority on diversifying project portfolio. In addition, it was shown that once a construction company becomes unstable in business operation, it is hard to recover even when the market condition turns for the better. This suggests that, to improve business operation of a construction company, internal capacity-building is as important as the market condition and other external circumstances.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.