• 유통과학연구
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• 제17권11호
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• pp.73-79
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• 2019

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.

• 지능정보연구
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• 제21권4호
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• pp.111-131
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• 2015

본 연구에서는 데이터마이닝 기법의 일종인 자기조직화지도(Self-Organizing Map, SOM)를 이용하여 비외감기업의 부실화 유형을 구분하고자 한다. 자기조직화지도는 인공 신경망을 기초로 자율학습을 통해 입력된 값을 유사한 군집끼리 묶어내는 방법으로, 기존의 통계적 군집 분류 방법보다 성능이 뛰어나고, 고차원의 입력데이터를 저차원으로 시각화할 수 있다는 장점 때문에 다양한 분야에서 각광받고 있다. 본 연구에서는 기존 연구의 주요 분석대상이었던 외감기업에 비해 부실화 빈도는 높지만 데이터 수집의 어려움으로 인해 분석대상에서 다소 제외되었던 비외감기업의 부실화 유형에 대해 알아보고, 유형별 구체적인 사례도 소개하고자 한다. 재무자료수집이 가능한 100개의 비외감 부실기업에 대해 분석한 결과, 비외감기업의 부실화 유형은 다섯 가지로 구분되었다. 유형 1은 전체 집단의 약 12%를 차지하며, 수익성, 성장성 등 재무지표가 다른 유형에 비해 열등하였다. 유형 2는 전체 집단의 약 14%로, 유형 1보다는 덜 심각하지만 재무지표가 대체로 열등하였다. 유형 3은 성장성 지표가 열등한 그룹으로 기업간 경쟁이 극심한 가운데 지속적으로 성장하지 못하고 부실화된 경우로 약 30%의 기업이 포함되었다. 유형 4는 성장성은 탁월하나 부채경영 등 과감한 경영으로 인해 유동성 부족이나 현금부족 등의 이유로 부실화된 그룹으로 약 25%의 기업이 포함되었다. 유형 5는 거의 모든 재무지표가 우수한 건전기업으로, 단기적인 경영전략의 실수 또는 중소기업의 특성상 경영자의 개인적 사정으로 부실화 되었을 가능성이 큰 그룹으로 약 18%의 기업이 포함되었다. 본 연구 결과는 부실화 유형을 구분하는데 기존의 통계적 방법이 아닌 자기조직화지도를 이용하였다는 점에서 학문적 의의가 있고, 비외감기업의 재무지표만으로도 1차적인 부실화 징후를 발견할 수 있다는 점에서 실무적 의의가 있다고 할 수 있다.

• 산경연구논집
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• 제8권4호
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• pp.47-52
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• 2017

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.

• The Journal of Asian Finance, Economics and Business
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• 제8권8호
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• pp.67-74
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• 2021

This study aims to examine and analyze the effect of Risk Profile, Good Corporate Governance (GCG), Earnings, Capital (RGEC), and Earnings per Share (EPS) on stock prices with financial distress as an intervening variable. The sampling technique used purposive sampling based on certain criteria and data used was secondary data, that is, annual reports of commercial banks in Indonesia for the period of 2012-2018 with a sample of 23 banks from a total population of 81 banks. This type of research is explanative with a quantitative descriptive approach to describe or explain quantitative data. The data obtained was analyzed using SEM (Structural Equation Model) with the AMOS Program. The results showed that RGEC, EPS, and financial distress affect stock prices. This is based on testing the direct effect as indicated by a p-value that is smaller than 0.05. Based on the mediation test, the results show that financial distress cannot mediate the effect of RGEC and EPS on stock prices as indicated by a p-value greater than 0.05. The implication of this research is very important for investors to analyze stock price changes based on RGEC, EPS, and financial distress to gain profits. In addition, there are various warning signs indicating that a company is experiencing financial distress or it is heading towards such a state. Being aware of these signs can help prevent failure.

• The Journal of Asian Finance, Economics and Business
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• 제8권8호
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• pp.1-12
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• 2021

Altman's Z-score is used to measure a company's financial health and to predict the probability that a company will collapse within 2 years. It is proven to be very accurate to forecast bankruptcy in a wide variety of contexts and markets. The goal of this study is to use Altman's Z-score model to forecast insolvency in non-financial publicly traded enterprises. Non-financial firms are a significant industry in Malaysia, and current trends of consolidation and long-term government subsidies make assessing the financial health of such businesses critical not just for the owners, but also for other stakeholders. The sample of this study includes 84 listed companies in the Kuala Lumpur Stock Exchange. Of the 84 companies, 52 are considered high risk, and 32 are considered low-risk companies. Secondary data for the analysis was gathered from chosen companies' financial reports. The findings of this study show that the Altman model may be used to forecast a company's financial collapse. It dispelled any reservations about the model's legitimacy and the utility of applying it to predict the likelihood of bankruptcy in a company. The findings of this study have significant consequences for investors, creditors, and corporate management. Portfolio managers may make better selections by not investing in companies that have proved to be in danger of failing if they understand the variables that contribute to corporate distress.

• The Journal of Asian Finance, Economics and Business
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• 제9권6호
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• pp.105-114
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• 2022

With the help of this study, we aim to investigate the influence of Financial Distress (FD) and information and communication technology (ICT) on the operating performance and efficiency of banks in the Indian banking sector. FD can be defined as a position in which a company or individual is not in a condition to fulfill their promise of paying their obligations on time. The term "financial distress" refers to a situation in which a corporation or individual is unable to keep their promise of paying their debts on time. In this work, panel data analysis (PDA) was used to analyze data from 33 Indian banks over ten years (2010 to 2019). According to the findings, FD has a positive and significant impact on bank operational performance and efficiency. The current study will give the banking industry a better understanding of how a bank's performance can be negatively impacted by distressing conditions that render it inefficient and ineffective. Second, it will show investors how the level of distress can have a significant impact on bank performance in the market, finally resulting in the loss of money invested.

• 한국산학기술학회논문지
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• 제17권10호
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• pp.520-526
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• 2016

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.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.382-388
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• 2011

Being aware of the risk in advance necessitates intricate processes but is feasible. Although previous studies have demonstrated high accuracy, their performance still leaves room for improvement. A self-organizing feature map (SOM) based neurofuzzy model is developed in this study to provide another alternative for forecasting corporate financial distress. The model is designed to yield high prediction accuracy, as well as reference rules for evaluating corporate financial status. As a database, the study collects all financial reports from listed construction companies during the latest decade, resulting in over 1000 effective samples. The proportion of "failed" and "non-failed" companies is approximately 1:2. Each financial report is comprised of 25 ratios which are set as the input variable s. The proposed model integrates the concepts of pattern classification, fuzzy modeling and SOM-based optimization to predict corporate financial distress. The results exhibit a high accuracy rate at 85.1%. This model outperforms previous tools. A total of 97 rules are extracted from the proposed model which can be also used as reference for construction practitioners. Users may easily identify their corporate financial status by using these rules.

• The Journal of Asian Finance, Economics and Business
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• 제9권9호
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• pp.229-239
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• 2022

The primary objective of the current study is to ascertain the effect of transparency and disclosure (T&D) on the value of banks operating in the Indian banking sector. It also includes finding the moderating impact of financial distress (FD) and environmental, social, and governance (ESG) on the association between T&D and the valuation of banks. The study employs Panel data analysis (PDA) to analyze data and produce novel results thereafter. The authors of the study have considered using data of secondary nature which is sourced from banks operating in the Indian banking industry. Data in the current study has been considered for ten financial years, i.e., 2010 to 2019. The results reveal that T&D positively impacts a firm's valuation. We have also found evidence that financial distress and ESG (Environmental, Social, and Governance) significantly impact the value of firms under the influence of T&D. As far as we are aware, no study of this kind has been done yet in any developing nation to determine the effect that T&D, FD, and ESG have on the value of Indian banks. This paper can help future researchers in their respective studies that will involve the study variables (FD, T&D, and ESG).

• 한국산학기술학회논문지
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• 제20권11호
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• pp.151-156
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• 2019

본 연구는 2017년 기준 매출액 상위 46개 외식 업체를 선정 후 이들 업체들의 재무 비율을 산출한 후 이를 변수로 활용하여 로짓 분석에 의한 부실 예측모형의 평가에 목적이 있다. 국내 46개 외식 업체의 14개 재무비율을 변수로 선정하여 로짓 분석에 의한 실증 분석을 실시하였으며 실증 분석 결과는 다음과 같다. 첫째, 14개 재무 비율 중 건전 외식 기업과 부실 외식 기업을 구분하는 재무 비율은 유동 비율, 매출액 영업 이익률, 자기 자본 순이익률, 영업 현금 흐름비율, 영업 이익 증가율 및 총자산 회전율로 총 7개로 나타났으며 다른 7개의 재무 비율( 부채 비율, 차입금 의존도, 영업 이익 대비 이자 보상 비율, 매출액 순이익률, 총자산 순이익률, 매출액 증가율, 당기순이익 증가율, 총자산 증가율)은 통계적으로 유의하지 않은 것으로 분석되었다. 둘째, 7개 재무 비율을 로짓 함수의 변수로 활용하여 건전 외식 기업과 부실 외식 기업을 구분하는 로짓 분석에 의한 부실 예측 모형의 예측력은 89.1%로 나타났다.