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    Dotcom Failure and Predictive Power of Financial Ratios

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    https://www.riss.kr/link?id=A103799969

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    This paper is a continued effort to alarm investors whether their investments are in good hands in case of more vulnerable companies, the dotcoms. I perform relatively simple multivariate discriminant analyses on the bankruptcy of dotcoms and provide evidence that the bankruptcy of dotcoms can be predicted using accounting-based financial ratios. The stepwise discriminant analysis shows extremely high cross-validation correct classification rate (96.3%). More striking results are from two arbitrarily chosen models: the stepwise five-variable model and the two-variable model. The former uses five variables from the stepwise discriminant procedure that show a significant mean difference between non-bankrupt and bankrupt dotcoms, while the latter uses two variables from the Altman’s model that displays a significant mean difference between the two groups of dotcoms. Both models report the overall correct classification rates of 82% and 84% respectively that are reasonably high when compared to those in prior studies ranging from 60% to 95%. Most striking is that the two-variable model is based on the Altman’s model that was originally developed only for manufacturing firms. Financial distress and failure have been an aching issue in Korean banking industry since 2011 and the present study suggests that the parsimonious models of bankruptcy forecasting may be a key to prevent investors and savers from entrusting their lifetime savings with failing technology firms and financial institutions.
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    This paper is a continued effort to alarm investors whether their investments are in good hands in case of more vulnerable companies, the dotcoms. I perform relatively simple multivariate discriminant analyses on the bankruptcy of dotcoms and provide ...

    This paper is a continued effort to alarm investors whether their investments are in good hands in case of more vulnerable companies, the dotcoms. I perform relatively simple multivariate discriminant analyses on the bankruptcy of dotcoms and provide evidence that the bankruptcy of dotcoms can be predicted using accounting-based financial ratios. The stepwise discriminant analysis shows extremely high cross-validation correct classification rate (96.3%). More striking results are from two arbitrarily chosen models: the stepwise five-variable model and the two-variable model. The former uses five variables from the stepwise discriminant procedure that show a significant mean difference between non-bankrupt and bankrupt dotcoms, while the latter uses two variables from the Altman’s model that displays a significant mean difference between the two groups of dotcoms. Both models report the overall correct classification rates of 82% and 84% respectively that are reasonably high when compared to those in prior studies ranging from 60% to 95%. Most striking is that the two-variable model is based on the Altman’s model that was originally developed only for manufacturing firms. Financial distress and failure have been an aching issue in Korean banking industry since 2011 and the present study suggests that the parsimonious models of bankruptcy forecasting may be a key to prevent investors and savers from entrusting their lifetime savings with failing technology firms and financial institutions.

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    참고문헌 (Reference)

    1 정희돈, "여객운송기업의 부실예측모형 개발 및 검증" 한국기업경영학회 13 (13): 17-31, 2006

    2 김병기, "부채비율과 채권등급의 관계에 관한 연구" 한국기업경영학회 19 (19): 153-177, 2012

    3 Altman, E. I., "Zeta Analysis : A New Model to Identify Bankruptcy Risk of Corporations" 1 (1): 29-51, 1977

    4 Scott, J., "The Probability of Bankruptcy : A Comparison of Empirical Predictions and Theoretical Models" 5 (5): 317-344, 1981

    5 Jackson, R., "The Performance of Insolvency Prediction and Credit Risk Models in the UK : A Comparative Study" 45 (45): 183-202, 2013

    6 Dambolena, I., "Ratio Stability and Corporate Failure" 35 (35): 1017-1026, 1980

    7 김병기, "R&D와 기업가치의 관계 - 기업규모, 부채비율 및 산업유형을 중심으로 분석-" 한국기업경영학회 15 (15): 25-43, 2008

    8 Altman, E. I., "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy" 23 (23): 589-609, 1968

    9 Beaver, W. H., "Financial Ratios as Predictors of Failure" 4 (4): 71-111, 1966

    10 Ohlson, J., "Financial Ratios and the Probabilistic Prediction of Bankruptcy" 18 (18): 109-131, 1980

    1 정희돈, "여객운송기업의 부실예측모형 개발 및 검증" 한국기업경영학회 13 (13): 17-31, 2006

    2 김병기, "부채비율과 채권등급의 관계에 관한 연구" 한국기업경영학회 19 (19): 153-177, 2012

    3 Altman, E. I., "Zeta Analysis : A New Model to Identify Bankruptcy Risk of Corporations" 1 (1): 29-51, 1977

    4 Scott, J., "The Probability of Bankruptcy : A Comparison of Empirical Predictions and Theoretical Models" 5 (5): 317-344, 1981

    5 Jackson, R., "The Performance of Insolvency Prediction and Credit Risk Models in the UK : A Comparative Study" 45 (45): 183-202, 2013

    6 Dambolena, I., "Ratio Stability and Corporate Failure" 35 (35): 1017-1026, 1980

    7 김병기, "R&D와 기업가치의 관계 - 기업규모, 부채비율 및 산업유형을 중심으로 분석-" 한국기업경영학회 15 (15): 25-43, 2008

    8 Altman, E. I., "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy" 23 (23): 589-609, 1968

    9 Beaver, W. H., "Financial Ratios as Predictors of Failure" 4 (4): 71-111, 1966

    10 Ohlson, J., "Financial Ratios and the Probabilistic Prediction of Bankruptcy" 18 (18): 109-131, 1980

    11 Alireza, F, "Evaluation of the Financial Ratio Capability to Predict the Financial Crisis of Companies" 9 (9): 57-69, 2012

    12 Deakin, E., "A Discriminant Analysis of Predictors of Business Failure" 10 (10): 167-179, 1972

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    학술지 이력

    학술지 이력
    연월일 이력구분 이력상세 등재구분
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    2017-01-01 등재 등재학술지 유지 (계속평가) KCI등재
    2013-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2010-01-01 등재 등재학술지 선정 (등재후보2차) KCI등재
    2009-03-13 학회명변경 영문명 : 미등록 -> Korean Corporation Management Association KCI등재후보
    2009-01-01 등재 등재후보 1차 PASS (등재후보1차) KCI등재후보
    2007-01-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
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    학술지 인용정보

    학술지 인용정보
    기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
    2016 1.56 1.56 1.63
    KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
    1.75 1.7 2.494 0.42
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