경영분석지표와 의사결정나무기법을 이용한 유상증자 예측모형 개발

김명균(Myeong-Kyun Kim),조윤호(Yoonho Cho) 한국지능정보시스템학회 2012 지능정보연구 Vol.18 No.4

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms’ growth, profitability, stability, activity, productivity, etc., and regularly report the firms’ financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction model

외식프랜차이즈기업 부실예측모형 예측력 평가

Si-Joong Kim 한국유통과학회 2019 유통과학연구 Vol.17 No.11

Purpose: The purpose of this study was evaluated to compare the predictive power of distress prediction models by using discriminant analysis method and logit analysis method for food service franchise industry in Korea. Research design, data and methodology: Forty-six food service franchise industry with high sales volume in the 2017 were selected as the sample food service franchise industry for analysis. The fourteen financial ratios for analysis were calculated from the data in the 2017 statement of financial position and income statement of forty-six food service franchise industry in Korea. The fourteen financial ratios were used as sample data and analyzed by t-test. As a result seven statistically significant independent variables were chosen. The analysis method of the distress prediction model was performed by logit analysis and multiple discriminant analysis. Results: The difference between the average value of fourteen financial ratios of forty-six food service franchise industry was tested through t-test in order to extract variables that are classified as top-leveled and failure food service franchise industry among the financial ratios. As a result of the univariate test appears that the variables which differentiate the top-leveled food service franchise industry to failure food service industry are income to stockholders’ equity, operating income to sales, current ratio, net income to assets, cash flows from operating activities, growth rate of operating income, and total assets turnover. The statistical significances of the seven financial ratio independent variables were also confirmed by logit analysis and discriminant analysis. Conclusions: The analysis results of the prediction accuracy of each distress prediction model in this study showed that the forecast accuracy of the prediction model by the discriminant analysis method was 84.8% and 89.1% by the logit analysis method, indicating that the logit analysis method has higher distress predictability than the discriminant analysis method. Comparing the previous distress prediction capability, which ranges from 75% to 85% by discriminant analysis and logit analysis, this study’s prediction capacity, which is 84.8% in the discriminant analysis, and 89.1% in logit analysis, is found to belong to the range of previous study’s prediction capacity range and is considered high number.

온라인 언급이 기업 성과에 미치는 영향 분석

정지선(Ji Seon Jeong),김동성(Dong Sung Kim),김종우(Jong Woo Kim) 한국지능정보시스템학회 2015 지능정보연구 Vol.21 No.4

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, ‘energy/chemical’, ‘consumer goods for living’ and ‘consumer discretionary’ showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as ‘information technology’ and ‘shipbuilding/transportation’ industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as ‘Kangwon Land’, ‘KT & G’ and ‘SK Innovation’ showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as ‘Young Poong’, ‘LG’, ‘Samsung Life Insurance’, and ‘Doosan’ had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

A Prediction of Stock Price Movements Using Support Vector Machines in Indonesia

Ervandio Irzky ARDYANTA,Hasrini SARI 한국유통과학회 2021 The Journal of Asian Finance, Economics and Busine Vol.8 No.8

Stock movement is difficult to predict because it has dynamic characteristics and is influenced by many factors. Even so, there are some approaches to predict stock price movements, namely technical analysis, fundamental analysis, and sentiment analysis. Many researches have tried to predict stock price movement by utilizing these analysis techniques. However, the results obtained are varied and inconsistent depending on the variables and object used. This is because stock price movement is influenced by a variety of factors, and it is likely that those studies did not cover all of them. One of which is that no research considers the use of fundamental analysis in terms of currency exchange rates and the use of foreign stock price index movement related to the technical analysis. This research aims to predict stock price movements in Indonesia based on sentiment analysis, technical analysis, and fundamental analysis using Support Vector Machine. The result obtained has a prediction accuracy rate of 65,33% on an average. The inclusion of currency exchange rate and foreign stock price index movement as a predictor in this research which can increase average prediction accuracy rate by 11.78% compared to the prediction without using these two variables which only results in average prediction accuracy rate of 53.55%.

제주지역 호텔기업 부실예측모형 평가

김시중 한국유통과학회 2017 The Journal of Industrial Distribution & Business( Vol.8 No.4

Purpose – This current study will investigate the average financial ratio of top and failed five-star hotels in the Jeju area. A total of 14 financial ratio variables are utilized. This study aims to; first, assess financial ratio of the first-class hotels in Jeju to establishing variables, second, develop distress prediction model for the first-class hotels in Jeju district by using logit analysis and third, evaluate distress prediction capacity for the first-class hotels in Jeju district by using logit analysis. Research design, data, and methodology – The sample was collected from year 2015 and 14 financial ratios of 12 first-class hotels in Jeju district. The results from the samples were analyzed by t-test, and the independent variables were chosen. This was an empirical study where the distress prediction model was evaluated by logit analysis. This current research has focused on critically analyzing and differentiating between the top and failed hotels in the Jeju area by utilizing the 14 financial ratio variables. Results – The verification result of the accuracy estimated by logit analysis has shown to indicate that the distress prediction model’s distress prediction capacity was 83.3%. In order to extract the factors that differentiated the top hotels in the Jeju area from the failed hotels among the 14 chosen, the analysis of t-black was utilized by independent variables. Logit analysis was also used in this study. As a result, it was observed that 5 variables were statistically significant and are included in the logit analysis for discernment of top and failed hotels in the Jeju area. Conclusions – The distress prediction press’ prediction capability was compared in this research analysis. The distress prediction press prediction capability was shown to range from 75-85% by logit analysis from a previous study. In this current research, the study’s prediction capacity was shown to be 83.33%. It was considered a high number and was found to belong to the range of the previous study’s prediction capacity range. From a practical perspective, the capacity of the assessment of the distress prediction model in the top and failed hotels in the Jeju area was considered to be a prominent factor in applications of future hotel appraisal.

빅데이터 분석을 통한 한독 상호협력 방안: 독일선교를 중심으로

황홍섭 행복한부자학회 2023 행복한부자연구 Vol.12 No.2

본 연구는 한독수교 140주년을 맞이하여 빅데이터 분석을 활용하여 양국관계를 돌아보며, 선교적 차원에서 건강하고 행복한 한독상호 협력 방안을모색하였다. 협력방안 모색을 위한 빅데이터를 수집하고, 정제하여 워드클라우드 분석, 네트워크 분석, 미래신호예측 분석을 실시하였다. 독일선교관련 워드클라우드 분석결과는 크게 주요어(주요 사역), 지역, 인물로 범주화할 수 있었다. 종교개혁 발상지와 개신교 출발지라는 주요어와루터라는 인물과 독일이라는 지역이 갖는 비중이 크게 부각되었다. 독일선교관련 네트워크 분석결과 중, 첫째, 중심성 네트워크 분석결과는일차적으로 지역과 인물이 강한 연결성을 보이면서, 이차적으로 선교 사역에적절한 주제를 중심으로 연결이 확대되고 있다. 이 과정에 이단들의 활동이두드러지게 나타나고 있다. 둘째, 응집성 네트워크 분석 결과, 선교지역관련그룹, 선교활동(사역)관련 그룹, 선교 인물관련 그룹으로 나타나, 중심성 네트워크 분석결과를 좀 더 명료하게 시각화됨을 알 수 있다. 독일선교관련 미래 신호예측분석 결과, 첫째, 선교관련 종합적으로 볼 때난민, 무슬림, 귀츨라프, 뒤셀도르프, 우크라이나가 약신호로 나타났다. 신호예측분석에 의하면 약신호는 강신호로 전환할 잠재력을 갖고 있어서 독일선교관련 이러한 사역이 행복한 선교 사역이 될 것으로 기대된다. 둘째, 독일선교와 관련된 지역에 대한 미래신호 예측결과, 필리핀, 캄보디아, 우크라이나, 인도네시아가 독일 선교관련 약신호로 나타나 앞으로 독일선교는 이러한지역을 선교 공동체의 거점으로 활용하는 것이 좋을 것으로 보인다. 결론적으로 3가지 분석 결과를 종합하면 한독양국은 난민 선교전략, 무슬림을 비롯한 창의적 접근지역 새로운 선교전략, 미전도 종족수준의 복음화율에 대한 새로운 선교전략 등 행복한 공동체성을 강화할 수 있는 총체적 선교전략을 동원해야 할 것이다. Christians become happy when they live a missionary life. Mission is a command and mission from Jesus, and when one obeys it, happiness and grace are given as gifts, making one’s life more valuable. On the occasion of the 140th anniversary of the establishment of diplomatic relations between Korea and Germany, this study looked back the relationship between the two countries using big data analysis and sought ways for healthy and happy mutual cooperation between Korea and Germany at a missionary level. Big data was collected and purified to find ways for cooperation, and word cloud analysis, network analysis, and future signal prediction analysis were conducted. The results of word cloud analysis related to German missions could be broadly categorized into key words (main ministry), region, and person. Germany is the birthplace of Martin Luther's Reformation and the birthplace of Protestantism, and the importance of the person Luther and the region of Germany is is strongly emphasised. Mission can be said to be a gift that brings happiness to the development of the region, nation, and world through people. Among the network analysis results related to German missions, firstly, the centrality network analysis results show a strong connection between regions and people, and secondarily, connections are expanding around temes suitable for missionary work. In this process, the activities of heretics are being revealed. Secondly, as a result of the cohesive network analysis, there are groups related to mission areas, groups related to missionary activities (ministry), and groups related to missionary people, which visualises the centrality network analysis results more clearly visualized. As a result of the analysis of predicting future signals related to German missions, first, when looking at missions in general, Refugees, Muslims, Gützlaff, Düsseldorf, and Ukraine appear as weak signals, and these weak signals have the potential to turn into strong signals, so these missions related to German missions I expect this to be a happy ministry. Second, as a result of predicting future signals for regions related to German missions, the Philippines, Cambodia, Ukraine, and Indonesia appear to have weak signals related to German missions, so it would be good for German missions to use these regions as a base for the missionary community in the future. In conclusion, combining the word cloud and network analysis results and the future signal prediction analysis results, Korea and Germany can strengthen a happy fellowship, including refugee mission strategies, new missionary strategies for creative approaches to areas including Muslims, and evangelization strategies for Europe, which has fallen to the level of unreached people groups. A comprehensive mission strategy needs to be mobilized.

Prediction of Soil Carbon Contents Using Smartphone Images and Multiple Regression Analysis

강윤구,손연규,이재한,천진혁,이창훈,오택근 한국토양비료학회 2022 한국토양비료학회지 Vol.55 No.3

Soil carbon is an important factor in the process of mitigating climate change and solving greenhouse gas problems. However, the previous technology for soil carbon content analysis required a lot of labor, time, and expensive equipment (i.e. an elemental analyzer). In this study, the disadvantages of previous analysis method were secured by using smartphone images and multiple regression analysis. To predict the soil carbon content, the color variables (e.g., RGB, CIE-L*a*b*, CIE-L*c*h*, and CIE-L*u*v*), gravimetric water content, and bulk density were used as statistical data. After Pearson’s correlation analysis, several variables that had high correlations were removed and then used. In addition, the result of variance inflation factor (VIF) analysis indicated that all variables should not cause multicollinearity problems. The predictive model was classified based on land use, and the predictive model for the entire sample was also derived. The adjusted coefficient ofdetermination (Adj. R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used to verify the predictive model. When the verification method was substituted for each predictive model, the reliability of the predictive model classified based on land use was high. Therefore, in order to predict thecarbon content in the agricultural soil, it is efficient to assign each prediction model after classifying agricultural land.

기업도산예측모형 설정에 관한 실증적 연구

정경희,조인희,조재립,송상민 한국경영공학회 2009 한국경영공학회지 Vol.14 No.2

Since the Asian financial crisis in 1997, the importance of measuring each firm's solvency and predicting the possibility of bankruptcy has been increased in the view of well-structured national economy and the valuation of each individual firm. Bankruptcy prediction techniques, which has been a hot issue for a long period among investing companies, is getting more sophisticated and approached in various ways. From late 1960s - when researchers started to test their bankruptcy prediction models - to 1970s, the main stream of prediction technique was multiple discriminant analysis and from early 1980s to late 1980s, the major used tool was logistic regression. Currently, artificial neural network is introduced and widely used for default prediction in the industry. Therefore, many different methods has been introduced and their effectiveness has been tested by the researchers; however, optimal model selection is difficult, as the used data, processes and definitions of bankruptcy vary. The study evaluated prediction accuracies of discriminant analysis. Since the Asian financial crisis in 1997, the importance of measuring each firm's solvency and predicting the possibility of bankruptcy has been increased in the view of well-structured national economy and the valuation of each individual firm. Bankruptcy prediction techniques, which has been a hot issue for a long period among investing companies, is getting more sophisticated and approached in various ways. From late 1960s - when researchers started to test their bankruptcy prediction models - to 1970s, the main stream of prediction technique was multiple discriminant analysis and from early 1980s to late 1980s, the major used tool was logistic regression. Currently, artificial neural network is introduced and widely used for default prediction in the industry. Therefore, many different methods has been introduced and their effectiveness has been tested by the researchers; however, optimal model selection is difficult, as the used data, processes and definitions of bankruptcy vary. The study evaluated prediction accuracies of discriminant analysis.

컨벤션 기업의 기업집단 분류 예측력 비교연구

김시중 ( Si Joong Kim ),홍경완 ( Kyung Wan Hong ) 한국이벤트컨벤션학회 2005 이벤트 컨벤션 연구 Vol.1 No.-

This study was conducted to evaluate he predictive power of classifying corporation group using logit and multiple discriminant analysis. 14 financial ratio of 17 convention corporations in 2004 was used as main variables. The statistical significances of the five financial ratio variables were confirmed by discriminant analysis and logit analysis. Considering all analysis(discriminant analysis, 94.1% & logit analysis, 82.3%), corporation group of classifying convention group (standard corporation vs. failure group) was confirmed to have a high predictive power by discriminant analysis.

원자력발전소 비상운전 직무의 오류 예측을 위한 정보적 분석

정원대,김재환,윤완철,Jeong, Won-Dae,Kim, Jae-Hwan,Yun, Wan-Cheol 대한인간공학회 1999 大韓人間工學會誌 Vol.18 No.3

More than twenty HRA (Human Reliability Analysis) methodologies have been developed and used for the safety analysis in nuclear field during the past two decades. However, no methodology appears to have universally been accepted, as various limitations have been raised for more widely used ones. One of the most important limitations of conventional HRA is insufficient analysis of the task structure and problem space. To resolve this problem, we suggest a framework of informational analysis for HRA. The proposed informational analysis consists of three parts. The first part is the scenario analysis that investigates the contextual information related to the given task on the basis of selected scenarios. The second is the goals-means analysis to define the relations between the cognitive goal and task steps. The third is the cognitive function analysis that identifies the cognitive patterns and information flows involved in the task. Through the three-part analysis. systematic investigation is made possible from the macroscopic information on the tasks to the microscopic information on the specific cognitive processes. It is expected that analysts can attain a structured set of information that helps to predict the types and possibility of human error in the given task.