This study, to find whether a sports activity and a speciality aptitude activity in school-childhood can affect in forming E.Q, has been done in Gyoung-Nam area and Busan wide city with asked 222 of men and women being in their school-childhood and a group of 85 people who had not joined in such activity, using a E.Q testing paper provided by Dae-Gyo Education Corp,. and Seoul National University Educational Research Institute. Following is the results after analyzing the compiled datas. 1. The E.Q. level difference between people who joined, and who not joined in a sports action activity was reported existing, the total E.Q average of those who joined was resulted 212.6, a point 29.6 higher than those not joined of 183.0 ( p< .05). As for the E.Q causing points, it resulted that the points of the joined group is generally up than that of the non-joined group, especially this difference was remarkable in terms of feeling recognition or feeling control, a finding that deserves an attention ( p<.05) 2. Joining periods of sports activity did also have relation to develping E.Q. of school-childhood according to this research, the total E.Q points of a group joined in the activity more than 2 years was 215.5 points, which was 17.4 points higher than those not joined of 186.5 points ( p< .05). Backing again to E.Q causing points in this case, it resulted without exeption of all main causes that those who joined in more than 2 years are generally higher than that of those joined less than 2 years, especially the difference was regarded as big in terms of feeling recognition or feeling control, a finding that deserves an attention ( p<.01). 3. The E.Q. differnce between those joined in a specialty aptitude activity and not joined was studied existing, the total E.Q average points of those joined in a specialty aptitude activity was 207.8, a higher figure by 21.3 points than those not joined group of 186.5 ( p< .05). As for the E.Q causing points, it resulted without exeption of all main causes that those who joined are generally higher than that of those not joined, especially for feeling recognition or feeling control, this difference was more clear, a finding that deserves an attention ( p<.01). 4. It also resulted that E.Q growth depends on the periods to have joined in a speciality aptitude activity, for example, the total E.Q points of those joined in the activity more than 2 years was 217.1, a total more higher by 13.5 points than 203.6 of those not joined ( p< .05). For the E.Q. causing points, it, with the exception of empathy was resulted that those who joined in the speciality aptitude activity more than 2 years are generally higher than those joined less than 2 years, especially the difference is remarkable in terms of feeling recognition or feeling control, a finding that is also remarkable ( p<.05). 5. The E.Q difference between the men and women who joined in both activities of sports & speciality aptitude was found existing, the total E.Q. average for women was resulted 214.2 points, which was 9.2 points higher than men of 205.0. As for the E.Q. causing points, which, without exeption of main causes, women's was reported being high than that of men, in special is more remarkable in terms of feeling control, a finding that deserves an attention. ( p<.05).
The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.