지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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Purpose - Going concern is one of fundamental concepts in accounting and auditing and sometimes the assessment of a company’s going concern status that is a tough process. Various going concern prediction models’ based on statistical and data mining methods help auditors and stakeholders suggested in the previous literature.
Research design - This paper employs a data mining approach to prediction of going concern status of Iranian firms listed in Tehran Stock Exchange using Particle Swarm Optimization. To reach this goal, at the first step, we used the stepwise discriminant analysis it is selected the final variables from among of 42 variables and in the second stage; we applied a grid-search technique using 10-fold cross-validation to find out the optimal model.
Results - The empirical tests show that the particle swarm optimization (PSO) model reached 99.92% and 99.28% accuracy rates for training and holdout data.
Conclusions – The authors conclude that PSO model is applicable for prediction going concern of Iranian listed companies.
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참고문헌 (24)
참고문헌 신청
Alici, Y. / 1996 / Neural network in corporate failure prediction: the UK experience / Proceedings of the Third International Conference on Neural Networks in the Capital Markets :
Alpaydin, E. / 2010 / Introduction to machine learning / MIT Press
Altman, E. I. / 1993 / Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting & Avoiding Distress and Profiting from Bankruptcy / John Wiley and Sons
Andres, J. / 2012 / Bankruptcy prediction models based on multinorm analysis : An alternative to accounting ratios / Knowledge-Based Systems 30 (6) : 48 ~ 56
Ashoori, S. / 2011 / Compare failure prediction models based on feature selection technique: empirical case from Iran / Procedia Computer Science 3 : 568 ~ 573
Alpaydin, E. / 2010 / Introduction to machine learning / MIT Press
Altman, E. I. / 1993 / Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting & Avoiding Distress and Profiting from Bankruptcy / John Wiley and Sons
Andres, J. / 2012 / Bankruptcy prediction models based on multinorm analysis : An alternative to accounting ratios / Knowledge-Based Systems 30 (6) : 48 ~ 56
Ashoori, S. / 2011 / Compare failure prediction models based on feature selection technique: empirical case from Iran / Procedia Computer Science 3 : 568 ~ 573
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