• Journal of Technology Innovation
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• v.17 no.2
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• pp.55-80
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• 2009

Technology innovation activity plays a pivotal role in constructing the entrance barrier for other firms and making process improvement and new product. and these activities give a profit increase and growth to firms. Thus, technology innovation activity can reduce the default risk of firms. However, technology innovation activity can also increase the firm's default risk because technology innovation activity requires too much investment of the firm's resources and has the uncertainty on success. The purpose of this study is to examine the effect of technology innovation activity on the default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Securities Market and The Kosdaq Market from January 1,2000 to December 31, 2008. This study makes use of R&D intensity as an proxy variable of technology innovation activity. The default probability which proxies the default risk of firms is measured by the Merton's(l974) debt pricing model. The main empirical results are as follows. First, from the empirical results, it is found that technology innovation activity has a negative and significant effect on the default risk of firms independent of the Korea Securities Market and Kosdaq Market. In other words, technology innovation activity reduces the default risk of firms. Second, technology innovation activity reduces the default risk of firms independent of firm size, firm age, and credit score. Third, the results of robust analysis also show that technology innovation activity is the important factor which decreases the default risk of firms. These results imply that a manager must show continuous interest and investment in technology innovation activity of one's firm. And a policymaker also need design an economic policy to promote the technology innovation activity of firms.

• Journal of Korea Technology Innovation Society
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• v.12 no.4
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• pp.715-739
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• 2009

Technology innovation activity plays an important role in increasing a sales by bringing on the improvement of product's performance and a profitability by reducing the cost of production. Thus, technology innovation activity can reduce the default risk of firms. However, in spite of these effects of technology innovation activity, this activity can make the default risk of firm because it induce a firm to much investment of resources. This study examines the effect of technology innovation activity on the sales, profitability, and default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Stock Exchange from January 1, 2000 to December 31, 2008. The results show that technology innovation activity has a positive effect on the sales (profitability) but a negative effect on the default risk of firms. Also there is the significant mediation effect of sales and profitability.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Technology Innovation
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• v.22 no.2
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• pp.185-220
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• 2014

The purpose of this study is to get a whole picture of financial conditions of the new and renewable energy sector which have been growing rapidly and predict bankruptcy risk quantitatively. There have been many researches on the methodologies for company failure prediction, such as financial ratios as predictors of failure, analysis of corporate governance, risk factors and survival analysis, and others. The research method for this study is Altman Z-score which has been widely used in the world. Data Set was composed of 121 companies with financial statements from KIS-Value. Covering period for the analysis of the data set is from the year 2006 to 2011. As a result of this study, we found that 38 percent of the data set belongs to "Distress" Zone (on alert) while 38% (on watch), summed into 76%, whose level could be interpreted to doubt about the sustainability. The average of the SMEs in wind energy sector was worse than that of SMEs in solar energy sector. And the average of the SMEs in the "Distress" Zone (on alert) was worse than that of the companies of large group in the "Distress" Zone (on alert). In conclusion, Altman Z-score was well proved to be effective for New & Renewable Energy Industry in Korea as a result of this study. The importance of this study lies on the result to demonstrate empirically that the majority of solar and wind enterprises are facing the risk of bankruptcy. And it is also meaningful to have studied the relationship between SMEs and large companies in addition to advancing research on new start-up companies.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.185-192
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• 2022

This study investigates the effect of the semi-supervised learning(SSL) method on predicting default risk of peer-to-peer(P2P) loans. Despite its proven performance, the supervised learning(SL) method requires labeled data, which may require a lot of effort and resources to collect. With the rapid growth of P2P platforms, the number of loans issued annually that have no clear final resolution is continuously increasing leading to abundance in unlabeled data. The research data of P2P loans used in this study were collected on the LendingClub platform. This is why an SSL model is needed to predict the default risk by using not only information from labeled loans(fully paid or defaulted) but also information from unlabeled loans. The results showed that in terms of default risk prediction and despite the use of a small number of labeled data, the SSL method achieved a much better default risk prediction performance than the SL method trained using a much larger set of labeled data.

• The Korean Journal of Financial Studies
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• v.11 no.1
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• pp.217-242
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• 2005

본 연구에서는 부도기업의 부도 후 회생여부와 부도발생시의 주식시장의 반응과의 관계를 조사하였다. 즉 증권시장이 부도기업의 사후적인 회생 또는 회생실패에 대한 통찰력을 부도시에 이미 갖고 있는지를 부도처리시의 주가반응을 분석함으로써 검정하고자 하는 것이다. 이를 위하여 외환위기 후 상장기업의 부도가 빈발하였던 1998년에서 2000년 사이에 부도가 발생한 상장회사 55개 기업을 대상으로 후에 회생한 기업(31개기업)과 그렇지 못한 기업(24개 기업)을 구분하여 후에 회생한 기업의 부도시의 주가반응이 회생하지 못한 기업의 부도시의 주가반응보다 덜 부정적이었는지를 검정하였다. 실증분석 결과 부도기업 중 후에 회생한 기업(31개기업)의 분석기간 ($-10{\sim}+10$)중 평균초과수익률과 누적평균초과수익률이 비회생기업(24개기업)의 그것에 대하여 유의한 (+)의 차이가 나타나지 않았다. 또한 부도기업의 누적초과수익률을 종속변수로 하고 회생여부를 나타내는 더미변수, 전년도감사의견이 적정의견인지의 여부, 부채비율, 총자산(억원) 자연 로그값, 사전적 폭로정보 대용변수로서의 지난 1년간 주가반응을 의미하는 (-230, -11)윈도우 누적초과수익률을 독립변수로 하여 다중회귀분석을 실시하였으나 부도후 회생여부를 나타내는 더미변수의 회귀계수는 유의적이지 않았다. 따라서 초과수익률 차이분석결과 회생기업의 부도시의 주가반응이 비회생기업의 그것에 비하여 유의한 (+)의 차이가 없고, 또한 회귀분석 결과 부도시의 초과수익률과 부도후 회생여부는 유의한 관계가 없으므로 부도처리시의 주가반응에서 후에 회생하는 기업이 그렇지 않은 기업보다 덜 부정적일 것이다라는 연구가설은 기각된다.등에 대한 평가기준의 재정립이 강구되어야 할 것이다.한 변동성에서 큰 위험프리미엄이라는 연결고리를 거쳐 코리아 디스카운트라는 현상으로 귀착되는 현상에 주목하고 있는 본 연구의 결과가 실무에서 유용하게 사용됨은 물론이요 또한 본 연구의 방법론 자체가 매우 정교하고 포괄적이어서 금융시계열을 포함한 다른 여러 분야에 크게 응용될 수 있는 외부효과도 기대된다.R 효과는 전통적 의미의 일반적으로 낮은 PER종목이 초과수익률을 내는 것이 아니라, 기업규모가 크더라도 그 기업의 개별특성을 고려했을 때 이와 비교해 상대적으로 PER가 낮은 종목에 투자하면 초과수익을 낼 수 있음을 의미한다. 발견하였다.적 일정하게 하는 소비행동을 목표로 삼고 소비와 투자에 대한 의사결정을 내리고 있음이 실증분석을 통하여 밝혀졌다. 투자자들은 무위험 자산과 위험성 자산을 동시에 고려하여 포트폴리오를 구성하는 투자활동을 행동에 옮기고 있다.서, Loser포트폴리오를 매수보유하는 반전거래전략이 Winner포트폴리오를 매수보유하는 계속거래전략보다 적합한 전략임을 알 수 있었다. 다섯째, Loser포트폴리오와 Winner포트폴리오를 각각 투자대상종목으로써 매수보유한 반전거래전략과 계속거래 전략에 대한 유용성을 비교검증한 Loser포트폴리오와 Winner포트폴리오 각각의 1개월 평균초과수익률에 의하면, 반전거래전략의 Loser포트폴리오가 계속거래전략의 Winner포트폴리오보다 약 5배정도의 높은 1개월 평균초과수익률을 실현하였고, 반전거래전략의 유용성을 충분히 발휘하기 위하여 장단기의 투자기간을 설정할 경우에 6개월에서 36개월로 이동함에 따라 6개월부터 24개월까지는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.41-58
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• 1998

본 연구는 여러 가지 Data Mining 기법들로부터 도출된 지식과 AHP를 이용하여 도출된 전문가의 지식을 사용된 정보의 특성에 따라 조사하고, 이러한 각각의 지식들을 중심으로 부도예측 모형을 설계한 후, 각 모형의 특성 및 부도예측력에 대한 실증적 비교연구에 그 목적을 두고 있다. 사용된 Data Mining 기법들은 통계적 다중판별분석 모형, ID3 모형, 인공신경망 모형이며, 전문가 지식의 추출은 AHP를 사용하여 45명의 전문가로부터 부도와 관련하여 인터뷰 및 설문조사를 실시하였다. 특히 부도예측에 사용된 변수의 특성을 정량적 재무정보와 정성적 비재무정보로 나누어서 각 모형의 특성을 비교연구하였다. 연구결과 부도예측시 정성적정보의 중요성을 확인하였으며, 전문가의 지식을 기반으로한 AHP 모형이 위험예측모형으로 사용될 수 있음을 실증적으로 보여주었다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.176-182
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• 2006

본 연구에서는 기존 Bagging Predictors에 수정을 가한 Modified Bagging Predictors를 이용하여 SOHO 에 대한 부도예측 모델을 제시한다. 대기업 및 중소기업에 대한 기압부도예측 모델에 대한 많은 선행 연구가 있어왔지만 SOHO 만의 기업부도예측 모델에 관한 연구는 미비한 상태이다. 금융기관들의 대출심사시 대기업 및 중소기업과는 달리 SOHO에 대한 대출심사는 이직은 체계화되지 못한 채 신용정보점수 등의 단편적인 요소를 사용하고 있는 것에 현실이고 이에 따라 잘못된 대출로 안한 금융기관의 부실화를 초래할 위험성이 크다. 본 연구에서는 실제 국내은행의 SOHO 데이터 집합이 사용되었다. 먼저 기업부도 예측 모델에서 우수하다고 연구되어진 인공신경망과 의사결정나무 추론 기법을 적용하여 보았지만 만족할 만한 성과를 이쓸어내지 못하여, 기존 기업부도예측 모델연구에서 적용이 미비하였던 Bagging Predictors와 이를 개선한 Modified Bagging Predictors를 제시하고 이를 적용하여 보았다. 연구결과,; SOHO 부도예측에 있어서 본 연구에서 제시한 Modified Bagging Predictors 가 인공신경망과 Bagging Predictors등의 기존 기법에 비해서 성과가 향상됨을 알 수 있었다. 제시된 Modified Bagging Predictors의 유용성을 확인하기 위해서 추가적으로 대수의 공개 데이터 집합을 활용하여 성능을 비교한 결과 Modified Bagging Predictors 가 기존의 Bagging Predictors 에 비해 일관적으로 성과가 향상됨을 알 수 있었다.

• Industry Promotion Research
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• v.9 no.2
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• pp.197-205
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• 2024

This study examines the impact of corporate ESG performance on debt default risk using a sample of Chinese A-share listed.The I mpact of ESG Performance on Debt Default Risk of Heavy Polluter Firms from 2012 to 2022. The findings show that good ESG performance can effectively reduce firms' debt default risk. Further analysis shows that firms' ESG performance reduces debt default risk by mitigating the impact of financing constraints. This study explores the influencing factors of debt default risk from the perspective of ESG performance, and also enriches the research on the economic impact of corporate ESG performance, providing empirical evidence for the prevention of corporate debt default risk.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.484-493
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• 2013

The Korean Government has provided public rental housing to stabilize civilian dwelling. However, unreliable management of public rental housing threatens the stability of residency. This study analyzes the default of public rental housing and the cause of default through a case study, which was intended for the residents in apartment complexes in danger of default. It also suggests countermeasures to cope with the problem of public rental housing. The results are listed as follows. First, rental housing apartments contribute a lot to the housing stability policy. On the other hand, the default of housing development, which is derived from the bankruptcy of housing management companies and the negligent control of government, brings about a serious problem for housing stability. Second, although the government has made a steady effort to solve this default problem, 9000 residents from 8 apartment complexes in Korea have experienced extreme unstable residency. Third, there are many causes for the default of public rental housing such as unqualified management companies and cursory monitoring by the government. The fundamental solution is to prevent public rental housing management companies from managing, or to build a new management system of public rental housing. To solve this problem, it's recommendable to delete the application term in the special law on the default of public rental housing so that it can be applied to all default apartments. If it is not possible to perform the policy for financial reasons, a new supply of civil housing provided by private companies needs to be re-examined completely or banned.