• Korean Business Review
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• v.18 no.2
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• pp.179-196
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• 2005

This study investigates whether the designated firm is affected by the financial characteristics prior to the korea stock exchange designating subject to administrative issues. that is, analyzes financial differences of the designated finn(AIF) from another firm(NAIF) in the same asset-scale and industry for 5 years prior to designating date. For this purpose, financial variables related with scale, profitability, growth nature, liquidity, stability, and active nature are chosen. 113 AIFs and 113 NIAFs are selected from listed stock on the korea stock exchange between 1991 and 1999. As a result, it is found that there are significant difference in all profitability, stability, active nature related financial variables for 5 years prior to each designation date. The difference is more significant as the designating date approaches. But, no significant difference is not found in all growth nature related financial variables for 5 years prior to each designation date. Liquidity related financial variables show significant difference in only the 1st year before designation date. To be short, the financial factors of profitability, liquidity, stability, and active nature have an effect on the designated firms.

• 한국벤처창업학회:학술대회논문집
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• 2021.11a
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• pp.185-187
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• 2021

관리종목 지정 제도는 상장 기업 내 기업의 부실화를 경고하여 기업에게는 회생 기회를 주고, 투자자들에게는 투자 위험을 경고하기 위한 시장규제 제도이다. 본 연구는 관리종목과 비관리종목의 기업의 재무 데이터를 표본으로 하여 관리종목 지정 예측에 대한 연구를 진행하였다. 분석에 쓰인 분석 방법은 로지스틱 회귀분석, 의사결정나무, 서포트 벡터 머신, 소프트 보팅, 랜덤 포레스트, LightGBM이며 분류 정확도가 82.73%인 LightGBM이 가장 우수한 예측 모형이었으며 분류 정확도가 가장 낮은 예측 모형은 정확도가 71.94%인 의사결정나무였다. 대체적으로 앙상블을 이용한 학습 모형이 단일 학습 모형보다 예측 성능이 높았다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.

• The Korean Journal of Nuclear Medicine Technology
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• v.26 no.2
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• pp.43-47
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• 2022

Purpose Radioimmunoassay implement quality control by systematizing the internal quality control system for quality assurance of test results. This study aims to contribute to the quality assurance of radioimmunoassay results and to implement systematic quality control by measuring the average CV of internal quality control and external quality control by plenty of institutions for reference when setting the laboratory's own acceptable range. Materials and Methods We measured the average CV of internal quality control and the bounce rate of more than 10.0% for a total of 42 items from October 2020 to December 2021. According to the CV result, we classified and compared the upper group (5.0% or less), the middle group (5.0~10.0%) and the lower group (10.0% or more). The bounce rate of 10.0% or more was compared by classifying the item of five or more institutions into tumor markers, thyroid hormones and other hormones. The average CV was measured by the overall average and standard deviation of the external quality control results for 28 items from the first quarter to the fourth quarter of 2021. In addition, the average CV was measured by the overall average and standard deviation of the proficiency results between institutions for 13 items in the first half and the second half of 2021. The average CV of internal quality control and external quality control was compared by item so we compared and analyzed the items that implement well to quality control and the items that require attention to quality control. Results As a result of measuring the precision average of internal quality control for 42 items of six institutions, the top group (5.0% or less) are Ferritin, HGH, SHBG, and 25-OH-VitD, while the bottom group (≤10.0%) are cortisol, ATA, AMA, renin, and estradiol. When comparing more than 10.0% bounce rate of CV for tumor markers, CA-125 (6.7%), CA-19-9 (9.8%) implemented well, while SCC-Ag (24.3%), CA-15-3 (26.7%) were among the items that require attention to control. As a result of comparing the bounce rate of more than 10.0% of CV for thyroid hormones examination, free T4 (2.1%), T3 (9.3%) showed excellent performance and AMA (39.6%), ATA (51.6%) required attention to control. When comparing the bounce rate of 10.0% or more of CV for other hormones, IGF-1 (8.8%), FSH (9.1%), prolactin (9.2%) showed excellent performance, however estradiol (37.3%), testosterone (37.7%), cortisol (44.4%) required attention to control. As a result of measuring the average CV of the whole institutions participating at external quality control for 28 items, HGH and SCC-Ag were included in the top group (≤10.0%), however ATA, estradiol, TSI, and thyroglobulin included in bottom group (≥30.0%). Conclusion As a result of evaluating 42 items of six institutions, the average CV was 3.7~12.2% showing a 3.3 times difference between the upper group and the lower group. Cortisol, ATA, AMA, Renin and estradiol tests with high CV will require continuous improvement activities to improve precision. In addition, we measured and compared the overall average CV of the internal quality control, the external quality control and the proficiency between institutions participating of six institutions for 41 items excluding HBs-Ab. As a result, ATA, AMA, Renin and estradiol belong to the same subgroup so we require attention to control and consider setting a higher acceptable range. It is recommended to set and control the acceptable range standard of internal quality control CV in consideration of many things in the laboratory due to the different reagents and instruments, and the results vary depending on the test's proficiency and quality control materials. It is thought that the accuracy and reliability of radioimmunoassay results can be improved if systematic quality control is implemented based on the set acceptable range.

• 프린팅코리아
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• s.29
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• pp.66-69
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• 2004

우리나라 최고의 인쇄사로 코스닥 상장 기업인 (주)보진재(대표 김정선)가 지난 4월 20일 관리종목으로 편입되면서 부도설 등 갖가지 루머에 시달렸다. 과거엔 관리종목으로 편입되면 대개 부도나 심각한 경영위기가 수반되었기 때문이다. 그러나 이번 보진재의 경우는 ‘관리종목 편입 규정’이 바뀌면서 어쩔 수 없이 발생한 일이었는데도 한동안 부도설과 M&A설 등이 난무했다. 김정선 사장을 만나 저간의 사정과 현재 보진재의 상황, 앞으로의 계획 등을 들어봤다.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.157-176
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• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.

• The Korean Journal of Financial Management
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• v.17 no.2
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• pp.49-70
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• 2000

본 연구에서는 한국주식시장의 대표적 주가지수인 KOSPI 200 진입기업과 제외기업의 주가 행태에 어떤 변화가 있는지를 분석하였다. 1994년 6월 이후부터 1999년 정기변경때까지의 기간에 KOSPI 200에 새로 진입한 종목과 제외된 종목을 검증표본으로 하고 이와 유사한 특성을 가지는 기업들을 대응표본으로 삼아 두 집단간에 나타나는 비정상수익률 및 누적비정상수익률의 특성과 비정상수익률과 비정상거래량간의 관계를 비교 분석하였다. 사건일의 비정상수익률과 사건기간동안의 누적비정상수익룰에 대한 분석결과는 KOSPI 200에 새로 포함되거나 제외되는 종목의 주가행태에 뚜렷한 변화가 있다는 결과를 보여주지는 못하고 있다. 그러나 일부 표본의 분석결과는 KOSPI 200에 새로 진입하거나 제외되는 정보가 공시일 이전에 시장에 반영되는 모습을 보여주며, 외환위기 이후기간에 발생한 정기변경진입종목에 나타난 주가행태 변화와 주가변동과 거래량 변동간의 관계는 일부 가격압박가설로 설명될 수 있음을 보여준다. 그러나 본 연구의 분석결과는 지수 신규편입 종목들이 펀드에 신규로 편입되는 과정에서 거래량이 증가해 초과수익이 발생한다는 기존의 가격압박가설의 내용을 충분히 지지하지는 못하고 있다.

• The Korean Journal of Financial Management
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• v.15 no.1
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• pp.139-163
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• 1998

본 연구는 KOSPI 200 주가지수선물이 현물시장의 유동성 및 변동성에 미치는 영향을 분석하기 위하여 1996년 5월 3일 주가지수선물의 도입 전 후 각각 6개월간의 일중 매수 매도호가, 일중 최고가, 최저가, 종가, 거래량에 대한 109개 기업의 패널자료(panel data)를 일반화최소승자(GLS) 방법에 의하여 시계열횡단면회귀분석(time series cross-sectional regression)으로 실시하였다. 본 연구에서 발견된 결과는 다음과 같다. 첫째, 주가지수선물 도입이후 주식시장 전반적으로 매수 매도호가 스프레드 증가는 발견할 수 없었다. 그러나 KOSPI 200 지수 비채택종목의 스프레드는 증가하여 주가지수선물 도입이후 유동성의 감소를 보였고 KOSPI 200 종목군은 유의적인 변화가 없었다. 둘째, 스프레드의 설명변수중 가격변수는 주가지수선물의 도입 이전에 유의적 설명변수이었고, 주가지수선물 도입이후에도 구조적 차이의 변화를 발견할 수 없었다. 그러나 스프레드의 설명변수 중 주가지수선물의 도입 이전에는 유의적이지 못하였던 변동성과 거래량의 스프레드에 대한 민감도가 주가지수선물 도입이후에는 유의적인 차이변화를 나타냈다. 변동성은 KOSPI 200 지수 비채택종목군에서, 그리고 거래량은 지수채택종목과 비채택종목군 모두에서 통계적으로 유의적인 차이 변화를 나타내어 주가지수선물 도입이후 스프레드의 설명변수에 구조적 변화가 발생하였다. 셋째, 주가지수선물의 도입이후 가격변수를 설명변수로 조정하고 난 현물시장의 변동성이 유의적으로 증가하였고, 특히 지수비채택종목군에서 더 심한 증가를 보여 주었다. 이는 선물가격이 정보를 효율적으로 반영하지 못하여 현물시장의 변동성에 다소 영향을 미친 것으로 볼 수 있다.

• The Korean Journal of Financial Management
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• v.15 no.1
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• pp.223-243
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• 1998

본 연구는 부실기업의 예측여부에 따른 금융기관의 주가 반응을 분석하였다. 1991년부터 1996년까지 관리종목에 편입된 종목중 40종목을 연구대상으로 선정하였다. 부실기업의 예측은 부실예측모형과 전문신용평가기관의 신용등급을 이용하여 판단하였다. 연구결과에 따르면 기업부실 공시시 금융기관 주식의 초과수익률은 전반적으로 부의 값을 갖는 것으로 분석되었다. 즉, 주가반응의 크기에는 정도의 차이는 있지만 부실예측 여부에 관계없이 기업부실은 금융기관 주가에 악영향을 미치는 것으로 나타났다. 구체적으로 살펴보면 신용등급에 의해 부실이 예측되는 경우에 비해 부실이 예측되지 못한 경우에 주가반응이 크고 유의적으로 나타났다. 그러나 부실예측모형을 이용한 경우에는 부실이 예측된 경우의 주가반응이 예측되지 못한 경우에 비해 크게 나타났다. 이러한 결과는 부실예측모형의 부정확성 또는 예측모형에서 사용된 회계자료의 부정확성에 기인한 것으로 판단된다.

• The Korean Journal of Financial Management
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• v.20 no.1
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• pp.261-277
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• 2003

The Korean Stock Market Surveillance System is desinged to control the volatility of stocks by drawing investor's attention and suppressing disguised demand, when stocks run up so rapidly in short period of time. Yet the Surveillance System has not been under empirical examination about its role and evolved in line with the Price Limit System. This study looks at the security returns under surveillance designation for 1995 -2001 period. The results indicate that the volatility of stocks has not been affected after surveillance designation. The constraints against the disguised demand, however, seems to limit the security returns rather than volatilities. These findings raises a question about the role of The Korean Stock Market Surveillance System for the control of volatility. The Surveillance System needs to be examined thoroughly about its role, function, and its conditions. Otherwise, the shareholders with less information could be placed at a disadvantage. This paper suggests that the system should be amended in an effort to make the volatility of stocks under control.