• 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.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.423-440
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• 2007

Although various models have been developed to establish the enterprise credit scoring, no model has utilized the enterprise human resource so far. The purpose of this study was to build an enterprise credit scoring model using enterprise human resource factors. The data to measure the enterprise credit score were made by the first-year research material of HCCP was used to investigate the enterprise human resource and 2004 Credit Rating Score generated from KIS-Credit Scoring Model. The independent variables were chosen among questionnaires of HCCP based on Mclagan(1989)'s HR wheel model, and the credit score of Korean Information Service was used for the dependent variables. The statistical method used for data analysis was logistic regression. As a result of constructing a model, 22 variables were selected. To see these specifically by each large area, 6 variables in human resource development(HRD) area, 15 in human resource management(HRM) area, and 1 in the other area were chosen. As a consequence of 10 fold cross validation, misclassification rate and G-mean were 30.81 and 68.27 respectively. Decile having the highest response rate was bigger than the one having the lowest response rate by 6.08 times, and had a tendency to decrease. Therefore, the result of study showed that the proposed model was appropriate to measure enterprise credit score using enterprise human resource variables.

• The Journal of Information Systems
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• v.29 no.1
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• pp.241-265
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• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.