한국산학기술학회논문지 (Journal of the Korea Academia-Industrial cooperation Society)

  • 제17권10호
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  • Pages.520-526
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  • 2016
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  • 1975-4701(pISSN)
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  • 2288-4688(eISSN)
한국산학기술학회 (The Korea Academia-Industrial cooperation Society)
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판별분석에 의한 기업부실예측력 평가: 서울지역 특1급 호텔 사례 분석

Evaluation of Corporate Distress Prediction Power using the Discriminant Analysis: The Case of First-Class Hotels in Seoul

  • 투고 : 2016.07.14
  • 심사 : 2016.10.07
  • 발행 : 2016.10.31
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초록

본 연구는 서울지역 특1급 호텔을 대상으로 2015년도 재무비율을 변수로 활용하여 표준재무비율을 산출하며, 다변량 판별분석에 의한 부실예측모형 개발 및 부실예측력 평가에 목적이 있다. 서울소재 19개 특1급 호텔의 14개 재무비율을 분석대상으로 선정하여 실증분석을 실시하였으며 분석결과는 다음과 같다. 첫째, 분석결과 우수기업과 부실기업을 판별하는 7개 재무비율은 유동비율, 차입금의존도, 영업이익대비 이자보상비율, 매출액영업이익율, 자기자본순이익율, 영업현금흐름비율, 총자산회전율로 나타났다. 둘째, 7개 재무비율을 활용하여 우수기업과 부실기업을 판별하는 판별함수를 다변량판별분석에 의해 추정하였으며, 추정된 판별함수를 실제 소속집단과 예측집단으로 분류가 가능한가의 예측력 검정 결과, 예측 판별력의 정확도는 87.9%로 분석되었다. 셋째, 추정된 판별함수의 예측 판별력의 정확도 검증결과 판별분석에 의한 부실예측모형의 예측력은 78.95%로 분석되었다. 이러한 분석결과, 호텔 경영진은 호텔기업의 부실기업집단을 판별하는 7개 재무비율을 중점적으로 관리해야 함을 시사하고 있다. 또한 호텔기업이 타 산업과는 뚜렷한 재무구조의 차이와 부실예측 지표가 상이하며, 이에 호텔기업 대상의 신용평가시스템 구축 시 호텔기업의 재무적 특성을 반영한 시스템 구축이 필요함을 시사하고 있다.

This study aims to develop a distress prediction model, in order to evaluate the distress prediction power for first-class hotels and to calculate the average financial ratio in the Seoul area by using the financial ratios of hotels in 2015. The sample data was collected from 19 first-class hotels in Seoul and the financial ratios extracted from 14 of these 19 hotels. The results show firstly that the seven financial ratios, viz. the current ratio, total borrowings and bonds payable to total assets, interest coverage ratio to operating income, operating income to sales, net income to stockholders' equity, ratio of cash flows from operating activities to sales and total assets turnover, enable the top-level corporations to be discriminated from the failed corporations and, secondly, by using these seven financial ratios, a discriminant function which classifies the corporations into top-level and failed ones is estimated by linear multiple discriminant analysis. The accuracy of prediction of this discriminant capability turned out to be 87.9%. The accuracy of the estimates obtained by discriminant analysis indicates that the distress prediction model's distress prediction power is 78.95%. According to the analysis results, hotel management groups which administrate low level corporations need to focus on the classification of these seven financial ratios. Furthermore, hotel corporations have very different financial structures and failure prediction indicators from other industries. In accordance with this finding, for the development of credit evaluation systems for such hotel corporations, there is a need for systems to be developed that reflect hotel corporations' financial features.

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