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4) Beaver, W. (1967). Financial ratios predictors of failure, Empirical research in accounting: selected

studies 1966. Journal of Accounting Research, 4, 71111.

5) Grice, G. S., & Dugan, M. T. (2001). The Limitations of Bankruptcy Prediction Models: Some Cautions

for Researcher. Review of Quantitative Finance and Accounting, 17, 151-166.

6) Haber, J. (2006). THEORETICAL DEVELOPMENT OF BANKRUPTCY, PREDICTION VARIABLES. The Journal of Theoretical Accounting Research, 2, 82-101

7) Kennedy, J., & Eberhart, R. C. ( 1995). Particle Swarm Optimization. Proceedings of IEEE

International Conference on Neural Networks, Piscataway, NJ. 1942-1948,.

8) Rajabioun, R. (2011). Cuckoo Optimization Algorithm. Applied Soft Computing, 11( 8), 55085518.

12

Abstract

In variant economic condition and volatility in the business environment, financial decisions are

more strategic than ever before and are always associated with risk and uncertainty. Hence, one way of

helping investors, in order to present information to their, is providing appropriate models to predict

the prospects of the company. This study, using data mining approach and Participle Swarm

Optimization and Cuckoo algorithm predict bankruptcy of companies in the Tehran Stock Exchange.

In the first step, 42 variables were selected based on previous research as proposals variables. After

selecting the sample companies, historical data were collected for one and two years ago. Then using

Participle Swarm Optimization 11 optimal variables algorithm were selected for entry into the model.

Subsequently, the research model was constructed using the Cuckoo algorithm. The results indicate

that this algorithm can predict company's bankruptcy with accuracy equal to 98.23%.