• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.173-181
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• 2021

In previous research, large companies were analyzed to focus on their past management performance and patent rights, but in this study we looked at the variability in future sales of small and medium-sized enterprises with patents through empirical analysis. We looked at how the quantitative and qualitative value of patent rights affect management performance of company. We used 'the number of patents' as the quantitative value of patents, 'the average score of patents' as the qualitative value of patents, and the average sales growth rate as the management performance of company. Through a discriminant analysis using the statistical program SPSS, both independent variables used in this study were significant for distinguishing between companies with an average growth in sales more than twice that of general small and medium-sized enterprises and those with less than twice the average sales growth rate. Therefore it is meaningful to provide stakeholders with an analysis framework on how sales will change in the future using the results of this study during guarantee or loan screening for small and medium-sized enterprises with patent rights.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.215-223
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• 2014

To provide evidence of bank's management consulting effect on client firms' performance and contribution to the bank, this study adopts Matching Sample Design and OLS regression using financial and consulting data from A Bank, which allowed us for empirically testing bank's management consulting effect on client firms. Specifically, We examine difference between ante-post performance of consulting client firms and difference between performance of consulting client firms and the other client firms. Empirical results show that management consulting by a Bank can increase interdependency, while It does not cause significant improvement of growth, activity, and profitability. This result provides further understanding of effect of management consulting by a bank on consulting client firms and some implication which banks should consider for management consulting.

• KDI Journal of Economic Policy
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• v.16 no.4
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• pp.47-80
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• 1994

본 연구는 우려나라 기업집단(企業集團)의 비용함수분석(費用函數分析)을 통해 제조업과 금융업간의 기술적 생산조건을 규명함으로써 제조산업(製造産業)과 금융업겸영(金融業兼營)의 효율성여부를 분석하고, 앞으로의 바람직한 금융소유지배규제정책방향(金融所有支配規制政策方向)을 모색하고 있다. 기업집단의 비용함수분석결과에 의하면 비금융제조업(非金融製造業)과 증권업(證卷業)간에는 범위(範圍)의 경제(經濟)(상호효율증진효과(相互效率增進效果))가 있는 반면 제조업(製造業)과 은행업(銀行業)이나 기타금융업과(其他金融業)의 사이에는 그런 관계가 부재(不在)하며, 우리나라 기업집단(企業集團)들은 평균적으로 규모가 과대하여 비효율(非效率)을 초래하고 있는 것으로 관찰되고 있다. 앞으로 우리 경제(經濟)의 개방화(開放化)와 금융자율화(金融自律化) 그리고 세계경제(世界經濟)의 통합화(統合化)가 진전됨에 따라서 제조업(製造業)과 증권업(證卷業)의 겸영(兼營)및 소유지배관계(所有支配關係)는 지속강화(持續强化)되겠지만 제조업(製造業)과 은행업(銀行業)과의 관계는 오히려 그 연계(連繫)가 약화(弱化)되며, 기업집단(企業集團)의 평균규모도 축소조정(縮小調整)될 것으로 전망된다. 이에 따라 장기적으로 금융소유규제정책(金融所有規制政策)은, 우선은 은행업과 제조업간의 소유지배를 허용하든 금지하든 어느 경우나 뚜렷한 자원배분(資源配分)의 왜곡(歪曲)이나 효율증진효과(效率增進效果)가 나타나지는 않기 때문에, 정부가 특정 소유구조를 강제하기보다는 은행(銀行)이나 기업(企業)들이 자체판단(自體判斷)에 의해 최적소유구조(最適所有構造)를 결정(決定)하도록 하되, 특정 소유지배관계(所有支配關係)의 형성이 금융산업 및 경제내에 경쟁저해적(競爭沮害的) 효과(效果)를 초래하지 않도록 하는 경쟁정책적(競爭政策的)차원에서 접근(接近)하는 것이 바람직할 것으로 판단된다. 그리고 소유집중(所有集中)에 따른 은행(銀行)의 안전(安全) 건전성(健全性) 저해효과(沮害效果)를 방지하기 위해서는 동일인(同一人) 여신한도(與信限度)의 엄격한 운용(運用) 등 건전감독기능(健全監督機能)을 보강해야 할 것이다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.91-102
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• 2020

This study constructed production frontiers of inputs and outputs in a sequential manner, measured inefficiencies by applying a non-radial sequential weighted Russell directional distance function to these frontiers, and analyzed Luenberg productivity indices and the contribution of each of input and output factor based on these distances. The results are as follows. First, the productivity of banks increased due to technical changes after the global financial crisis. Second, productivity growth decreased between 2009 and 2014 due to technical changes after the recession, as previous studies have shown that technology progressed before the global financial crisis but then largely decreased or remained the same thereafter. After 2014, the productivity of banks improved. This result may be due to both technology improvement after 10 years of stagnation and reduction of inputs and non-performing loans. Third, the 3.6% annual of productivity growth for 10 years was comprised of 1.77% household loans, 0.67% corporate loans, 0.98% manpower, 1.18% non-performing loans, -0.5% total deposits, and -1.25% securities. Finally, this study has limitations since it could not control risks such as capital structure and interest volatility.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.277-282
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• 2019

This study analyzed the determinants of the financial Soundness of savings banks. In particular, empirical analysis was carried out on how the loan deposit ratio correlates with financial soundness after restructuring. As the restructuring of savings banks was finalized in 2014, asset management changed and it is time to analyze the financial characteristics of savings banks. In summary, the relationship between the savings bank lending rate and the NPL ratio is estimated to have a negative value at the 1% significance level. In other words, the higher the mortgage rate, the lower the substandard and below ratio. It can be said that it is not easy for a savings bank to build an aggressive loan portfolio. In other words, the more difficult it is to finance loans through savings deposits, the more likely the risk aversion tends to be. The higher the corporate loan ratio, average interest expense, and economic growth rate, the higher the risk index. The larger the asset size and the higher the loan growth rate, the lower the risk index. Increasing the mortgage rate may reduce risk-seeking behavior, but it does not mean that it is unconditionally positive for savings banks. Therefore, the loan deposit rate regulation should reduce the incentives for excessive asset expansion and manage preemptive soundness through lending portfolio management.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.111-122
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• 2023

The purpose of this study is to verify the factors affecting survival time by estimating survival rate and survival time using non-financial information of social enterprises using credit guarantee in credit guarantee institutions, and provide information to stakeholders to improve survival rate and employ to contribute to maintaining and expanding the As a research method, survival analysis was performed using a non-parametric analysis method, Kaplan-Meier Analysis. As a sample, 621 companies (577 normal companies, 44 insolvent companies) established between 2009 and 2018 were selected as the target companies. As a result of examining the factors affecting survival time by classifying social enterprise representative information and corporate information, representative credit rating, representative home ownership, credit transaction period, and corporate credit rating were derived as significant variables affecting survival time. In the future, financial institutions will be able to induce corporate soundness by reflecting factors that affect survival when examining loans for social enterprises, contributing to job retention and reduction of social costs. Supporting organizations such as the government and private organizations will be able to use it in various ways, such as policy establishment and education and training for the growth and sustainability of social enterprises. With this study as an opportunity, I hope that research will continue with more interest in the factors influencing social enterprise performance as well as corporate insolvency.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.2 no.2
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• pp.93-120
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• 2007

To promote the commercialization of high technology based start-up company, it is essential to activate technology trading for the Innovative Small & Medium companies which eager to acquire technology competitiveness through technology trading and facilitate financial system which provide Small & Medium companies with technology commercialization money. This study focus on enhancing credibility of technology valuation solutions through the improvement of the existing technology valuation solutions. The existing technology valuation solutions in practice have deficiency in reasonable valuation because of subjective technology factor's calculation. And the commercialization risk of high technology based start-up companies cannot be fully reflected in the existing valuation solutions. The high risk of technology commercialization acts as a hurdle in the facilitation of providing money for the start-up companies. The improved new model will have more acceptable objectivity and reasonably reflect the commercialization risk of start-up companies. The new model has two distinctive features compared to existing solutions. The new model newly adopts commercialization success factor which reflects the risk of high technology based start-up company. And the new model excludes technology factor which is controversial among interested parties. The exclusion of technology factor will improve the objectivity of technology valuation and the adoption of commercialization success factor will solicit investor and capitalist who concern the high risk of technology based start-up companies. In conclusion, the improved new model is expected to activate technology trading and facilitate the money market through which high technology based start-up companies raise commercialization money.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.333-340
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• 2023

Efficient prediction of corporate bankruptcy is an important part of making appropriate lending decisions for financial institutions and reducing loan default rates. In many studies, classification models using artificial intelligence technology have been used. In the financial industry, even if the performance of the new predictive models is excellent, it should be accompanied by an intuitive explanation of the basis on which the result was determined. Recently, the US, EU, and South Korea have commonly presented the right to request explanations of algorithms, so transparency in the use of AI in the financial sector must be secured. In this paper, an artificial intelligence-based interpretable classification prediction model was proposed using corporate bankruptcy data that was open to the outside world. First, data preprocessing, 5-fold cross-validation, etc. were performed, and classification performance was compared through optimization of 10 supervised learning classification models such as logistic regression, SVM, XGBoost, and LightGBM. As a result, LightGBM was confirmed as the best performance model, and SHAP, an explainable artificial intelligence technique, was applied to provide a post-explanation of the bankruptcy prediction process.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.89-94
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• 2023

Recently, credit risk in the Chinese corporate bond market has increased significantly, and there is a possibility that banks that have invested in corporate bonds may become insolvent. The purpose of this study is to empirically analyze the effect of Chinese commercial banks' investment in securities on financial performance. The analysis results are as follows. First, it is estimated that as the share of securities investment by Chinese commercial banks increases, the bank's profitability decreases. It was found that investment in securities did not have a positive impact on profitability due to the increase in credit risk in the corporate bond market and the increase in marginal companies. Second, it is estimated that as the proportion of securities investment by Chinese commercial banks increases, the bank's soundness deteriorates. As credit risk in China's capital market is increasing, continuous management of non-performing assets is required. Chinese commercial banks need portfolio management through securities investment in addition to loan assets to improve profitability. However, volatility should be managed by adjusting the scale of securities management to an appropriate level.

• Journal of Korea Technology Innovation Society
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• v.20 no.2
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• pp.292-312
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• 2017

The purposes of this research are to verify: first, if the technology appraisal model reflects the company's management performance and the rates of bankruptcy and overdue; second, if the existing classification system of technology levels is suitable; and third, which is the most important appraisal factor that defines the classification system of technology levels. As a result of the analysis, financial performance (stability) and non-financial performance (technology environment) proved to be significant variables in explaining technology ratings. According to the verification of the suitability of classification system, it appeared that there is a significant difference in all appraisal items of all groups. The result of neural networks model verification indicates that the most important variable was the R&D capacity, the second variables which determine the suitability of technology financing were indicators related to the company management. The second variables which determine a company's technological excellence were a company's technological base. To summarize, the technology appraisal model not only reflects both managerial performance and risks of a company, but also anticipates the future by converging the management competence and technological competitiveness into R&D capacity. This implies that if the 'forward-looking' technology appraisal model is integrated into the existing, credit rating model, the appraisal model may have positive impact on improving anticipation and stability.