• Management Science and Financial Engineering
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• v.14 no.2
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• pp.89-104
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• 2008

Growth curves including Bass, Logistic and Gompertz functions are widely used in forecasting the market demand. Nonlinear least square method is often adopted for estimating the model parameters but it is difficult to set up the starting value for each parameter. If a wrong starting point is selected, the result may lead to erroneous forecasts. This paper proposes a method of selecting starting values for model parameters in estimating some growth curves by nonlinear least square method through grid search and transformation into linear regression model. Resealing the market data using the national economic index makes it possible to figure out the range of parameters and to utilize the grid search method. Application to some real data is also included, where the performance of our method is demonstrated.

• KDI Journal of Economic Policy
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• v.38 no.3
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• pp.1-15
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• 2016

Improving the quality of school education is one of the key policy concerns in Korea. This paper examines whether providing schools with adequate autonomy and incentives can meet the policy goals by looking at a recent policy reform in Korea. In 2009, the Korean government granted autonomy to certain private high schools on the condition that no financial subsidies would be provided to the schools. Because the autonomous private high schools cannot receive a subsidy, they have a strong incentive to meet parental demands because schools failing to meet these demands will lose students and will have to close. Applying the value-added model to longitudinal data at the student level, I find that students entering these autonomous schools show faster growth in their academic achievement than their peers in traditional non-autonomous schools. These results suggest that providing schools with autonomy and incentives can be a useful policy tool for improving school education.

• Asian Oncology Nursing
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• v.11 no.3
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• pp.193-199
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• 2011

Purpose: This study was aimed to examine mothers'knowledge about human papillomavirus (HPV)vaccination to prevent cervical cancer in Korea. Methods: From September 20 to October 10 2011, 101 mothers who have adolescent girls were surveyed with questionnaires about their general characteristics, the knowledge of HPV vaccine, inoculation rate and vaccination-related factors of their daughters. Results: The percentage of correct answers for HPV vaccine knowledge was 24.2% and the HPV vaccination rate was only 5.9%. HPV vaccine knowledge score of the vaccination group was significantly higher than that of the non-vaccinated group. Mothers said that the reasons why they didn't vaccinate their daughters against the HPV was the financial burden, the lack of HPV knowledge, and worries about possible side effects. The participants addressed that they understood the appropriate age for vaccination was sixteen. Conclusion: We recommend that more educational and promotional efforts need to be given for mothers in order to improve their knowledge of HPV vaccination and to increase the performance rates of HPV immunization against cervical cancer for their daughters.

• Management Science and Financial Engineering
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• v.20 no.2
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• pp.7-12
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• 2014

Content Delivery Network (CDN) has evolved to overcome a network bottleneck and improve user perceived Quality of Service (QoS). A CDN replicates contents from the origin server to replica servers to reduce the overload of the origin server. CDN providers would try to achieve an acceptable performance at the least cost including the storage space or processing power. In this paper, we introduce a new optimization model for the CDN design problem considering the user perceived QoS and single path (non-bifurcated) routing constraints and analyze the computational complexity for some special cases.

• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.27-34
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• 2019

We study the effect of detrending on the coherency between two time series processes. Many economic and financial time series variables include nonstationary components; however, we analyze the two most popular cases of stochastic and deterministic trends. We analyze the asymptotic behavior of coherency under incorrect detrending, which includes the cases of first-differencing the deterministic trend process and, conversely, the time trend removal of the unit root process. A simulation study is performed to investigate the finite sample performance of the sample coherency due to incorrect detrending. Our work is expected to draw attention to the possible distortion of coherency when the series are incorrectly detrended. Further, our results can extend to various specification of trends in aggregate time series variables.

• Journal of applied mathematics & informatics
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• v.39 no.1_2
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• pp.239-249
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• 2021

This study attempts to predict the price of gold futures, a real financial product, using ARIMA and LSTM. The wavelet analysis was applied to the data to predict the price of gold futures through LSTM and ARIMA. As results, it is confirmed that the prediction performance of the existing model of predict was improved. the case of predict of price of gold futures, we confirmed that the use of a deep learning model that is not affected by the non-stationary series data is suitable and the possibility of improving the accuracy of prediction through wavelet analysis.

• Annual Conference of KIPS
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• 2020.05a
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• pp.435-438
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• 2020

시장예측 문제를 해결하기 위하여 과거부터 꾸준한 연구가 진행되어왔다. 하지만 금융 시계열 데이터에는 분산이 일정하지 않으며 Non-stationarity 등 예측을 하는 것에 있어서 여러 가지 방해 요인이 존재한다. 또한 광범위한 데이터 변수는 기존에 사람이 직접 경험적으로 선택하는 것에 한계가 있기 때문에, 모델이 변수를 자동으로 추출할 수 있어야 한다. 본 논문에서는 여러 가지 금융 시계열 데이터의 문제를 고려하여 타임 스텝 정규화를 제안하며 자동 변수 추출을 위해 LSTM 형태의 오토 인코더 모델을 학습하였으며 LSTM 네트워크를 이용하여 시장 예측하는 모델을 제안한다. 해당 시스템은 실제 주식 거래나 시장 거래를 위하여 온라인 학습이 가능하며 긴 기간을 테스트 구간으로 실험한 결과 미래의 수익률을 예측하는 것에 있어서 우수한 성능을 보였다.

• Journal of Venture Innovation
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• v.7 no.2
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• pp.1-17
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• 2024

This study analyzed the effect of moving into an industrial park on the performance of a company using data from individual companies. In addition, regional variables were additionally set, and industrial complexes were divided into metropolitan and non-metropolitan areas for comparison. Data were collected through KIS-2022 (manufacturing) to verify the hypothesis. In the case of the analysis method, multiple regression analysis and Propensity Score Matching(PSM) were first used. The analysis results are as follows. First, it was found that companies that moved into industrial park had a positive effect on innovation performance than companies that did not move in. Second, in terms of financial performance, there was no statistically significant difference between companies that moved into the industrial park and those that did not. Third, there was a significant difference between companies that moved into industrial park and those that did not, depending on the metropolitan and non-metropolitan areas. Based on these analysis results, policy and academic implications could be presented.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Convergence Society for SMB
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• v.1 no.1
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• pp.1-8
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• 2011

With a sample of 147 Korean small and medium size companies, this study examined the relationships among degree of information orientation, corporate culture, degree of information management implementation and selected business performances in the process of implementing IMS(Information Management System). Information orientation is defined as company-wide understanding and implementation of the underlying philosophy, principles, approached, and tools of information improvement programs. It is assumed that successful implementation of information improvement programs requires a information-oriented mind-set of the employees. It is also assumed that successful implementation of information improvement programs require strong support from s corporate culture that emphasizes continues improvement. Adopting the competing values model of Quinn and McGrath(1985), corporate culture is classified into 'flexible' versus 'controlled culture' and 'outer-directed' versus 'inner-directed culture'. This study examined how such fitness influenced the implementation of information innovation programs and business performance. Implementation of information innovation programs was measured through various factors, such as leadership, strategic information planning, human resources focus, customer and market focus, process management, and information analysis and application. Business performance was measured through non-financial performance measuresm such as employee results, process results, information results, and customer results, and through perceived financial performance measures.