طراحی سیستم پیش‌بینی کننده عملکرد مالی در شرکت‌های صنعتی بر مبنای روش‌های داده‌کاوی

Altman E. (1968), Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance, vol. 23, p. 589–609.

Bagheri A., Mohammadi H. and Akbari M. (2015) Financial Forecasting Using ANFIS Networks with Quantum-behaved Particle Swarm Optimization, Expert Systems with Applications, vol. 42, pp. 1325-1339.

Beaver, W. (1966) Financial ratios as predictors of failure, Journal of Accounting Research, pp. 71-11.

Bernstein L. (1999) Analysis of financial statements, McGraw-Hill.

Burke R.t, Kristian J. and Benjamin C. (1997) The FindMe approach to assisted browsing, IEEE Intelligent Systems, vol. 12, no. 4, pp. 32-40.

Chapman P., Clinton J., Kerber R., Khabaza T. (1999) CRISP-DM 1.0: Step-by-Step data mining guide, SPSS Inc.

Delen D., Kuzey C. and Uyar A. (2013) Measuring firm performance using financial ratios: A decision tree approach, Expert Systems with Applications, no. 40, pp. 3970-3983.

Geng R., Bose I. and Chen X. (2015) Prediction of financial distress: An empirical study of listed Chinese companies using data mining, European Journal of Operational Research, vol. 240, no. 1, p. 258–268.

Han J. and Kamber J. P. M. (2011) Data Mining: Concepts and Techniques, Elsevier.

Lam M. (2004) Neural network techniques for financial performance prediction: integrating fundamental and technical analysi, Decision Support Systems, vol.37, p. 567-581.

Li Y., Lu L. and Xuefeng L. (2005) A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in e-Commerce, Expert Systems with Applications, vol. 28, pp. 67-77.

Kumar P. R. and Ravi V. (2007) Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review, European Journal of Operational Research, vol. I, no. 180, p. 1–28.

Resnick P. and Varian R. (1997) Recommender Systems, Communications of the ACM, pp. 56-58.

Ross S. A., Westerfield R. W., Jordan B. D. (2003) Fundamentals of corporate finance (6^th ed.), New York: The McGraw-Hill.

Spangler W. E., May J. and Vargas L. (1999) Choosing data mining methods for multiple classification: Representational and performance measurement implications for decision support, Journal of Management Information Systems, vol. 16, no. 1, pp. 37-62.

Sun J. and Li H. (2008) Data mining method for listed companies’ financial distress prediction, Knowledge-Based Systems, vol. 1, pp. 1-5.

Ting-Peng L. (2008) Recommendation systems for decision support: An editorial introduction, Decision Support Systems, vol.28, pp. 67-77.

Venugopal V. and Baets W. (1994) Neural networks and their applications in marketing management, Journal of Systems Management, vol. 45, no. 9, pp. 16-21.

Wanke P., Barros C. P. and Faria J. R. (2015) Financial distress drivers in Brazilian banks: A dynamic slacks approach, European Journal of Operational Research, vol. 240, pp. 258-268.

Zibanezhad E. and Foroghi M. D. (2011) Applying Decision Tree to Predict Bankruptcy. Computer Science and Automation Engineering (CSAE), IEEE International Conference, vol. 4, pp. 165-169.

Zopounidis C. and Dimitras A. I. (1998) Multicriteria decision aid methods for the prediction of business failure, Springer.

 

 

 

 

 

Altman E. (1968), Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance, vol. 23, p. 589–609.

Bagheri A., Mohammadi H. and Akbari M. (2015) Financial Forecasting Using ANFIS Networks with Quantum-behaved Particle Swarm Optimization, Expert Systems with Applications, vol. 42, pp. 1325-1339.

Beaver, W. (1966) Financial ratios as predictors of failure, Journal of Accounting Research, pp. 71-11.

Bernstein L. (1999) Analysis of financial statements, McGraw-Hill.

Burke R.t, Kristian J. and Benjamin C. (1997) The FindMe approach to assisted browsing, IEEE Intelligent Systems, vol. 12, no. 4, pp. 32-40.

Chapman P., Clinton J., Kerber R., Khabaza T. (1999) CRISP-DM 1.0: Step-by-Step data mining guide, SPSS Inc.

Delen D., Kuzey C. and Uyar A. (2013) Measuring firm performance using financial ratios: A decision tree approach, Expert Systems with Applications, no. 40, pp. 3970-3983.

Geng R., Bose I. and Chen X. (2015) Prediction of financial distress: An empirical study of listed Chinese companies using data mining, European Journal of Operational Research, vol. 240, no. 1, p. 258–268.

Han J. and Kamber J. P. M. (2011) Data Mining: Concepts and Techniques, Elsevier.

Lam M. (2004) Neural network techniques for financial performance prediction: integrating fundamental and technical analysi, Decision Support Systems, vol.37, p. 567-581.

Li Y., Lu L. and Xuefeng L. (2005) A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in e-Commerce, Expert Systems with Applications, vol. 28, pp. 67-77.

Kumar P. R. and Ravi V. (2007) Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review, European Journal of Operational Research, vol. I, no. 180, p. 1–28.

Resnick P. and Varian R. (1997) Recommender Systems, Communications of the ACM, pp. 56-58.

Ross S. A., Westerfield R. W., Jordan B. D. (2003) Fundamentals of corporate finance (6^th ed.), New York: The McGraw-Hill.

Spangler W. E., May J. and Vargas L. (1999) Choosing data mining methods for multiple classification: Representational and performance measurement implications for decision support, Journal of Management Information Systems, vol. 16, no. 1, pp. 37-62.

Sun J. and Li H. (2008) Data mining method for listed companies’ financial distress prediction, Knowledge-Based Systems, vol. 1, pp. 1-5.

Ting-Peng L. (2008) Recommendation systems for decision support: An editorial introduction, Decision Support Systems, vol.28, pp. 67-77.

Venugopal V. and Baets W. (1994) Neural networks and their applications in marketing management, Journal of Systems Management, vol. 45, no. 9, pp. 16-21.

Wanke P., Barros C. P. and Faria J. R. (2015) Financial distress drivers in Brazilian banks: A dynamic slacks approach, European Journal of Operational Research, vol. 240, pp. 258-268.

Zibanezhad E. and Foroghi M. D. (2011) Applying Decision Tree to Predict Bankruptcy. Computer Science and Automation Engineering (CSAE), IEEE International Conference, vol. 4, pp. 165-169.

Zopounidis C. and Dimitras A. I. (1998) Multicriteria decision aid methods for the prediction of business failure, Springer.

 

 

 

 

 

Altman E. (1968), Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance, vol. 23, p. 589–609.

Bagheri A., Mohammadi H. and Akbari M. (2015) Financial Forecasting Using ANFIS Networks with Quantum-behaved Particle Swarm Optimization, Expert Systems with Applications, vol. 42, pp. 1325-1339.

Beaver, W. (1966) Financial ratios as predictors of failure, Journal of Accounting Research, pp. 71-11.

Bernstein L. (1999) Analysis of financial statements, McGraw-Hill.

Burke R.t, Kristian J. and Benjamin C. (1997) The FindMe approach to assisted browsing, IEEE Intelligent Systems, vol. 12, no. 4, pp. 32-40.

Chapman P., Clinton J., Kerber R., Khabaza T. (1999) CRISP-DM 1.0: Step-by-Step data mining guide, SPSS Inc.

Delen D., Kuzey C. and Uyar A. (2013) Measuring firm performance using financial ratios: A decision tree approach, Expert Systems with Applications, no. 40, pp. 3970-3983.

Geng R., Bose I. and Chen X. (2015) Prediction of financial distress: An empirical study of listed Chinese companies using data mining, European Journal of Operational Research, vol. 240, no. 1, p. 258–268.

Han J. and Kamber J. P. M. (2011) Data Mining: Concepts and Techniques, Elsevier.

Lam M. (2004) Neural network techniques for financial performance prediction: integrating fundamental and technical analysi, Decision Support Systems, vol.37, p. 567-581.

Li Y., Lu L. and Xuefeng L. (2005) A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in e-Commerce, Expert Systems with Applications, vol. 28, pp. 67-77.

Kumar P. R. and Ravi V. (2007) Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review, European Journal of Operational Research, vol. I, no. 180, p. 1–28.

Resnick P. and Varian R. (1997) Recommender Systems, Communications of the ACM, pp. 56-58.

Ross S. A., Westerfield R. W., Jordan B. D. (2003) Fundamentals of corporate finance (6^th ed.), New York: The McGraw-Hill.

Spangler W. E., May J. and Vargas L. (1999) Choosing data mining methods for multiple classification: Representational and performance measurement implications for decision support, Journal of Management Information Systems, vol. 16, no. 1, pp. 37-62.

Sun J. and Li H. (2008) Data mining method for listed companies’ financial distress prediction, Knowledge-Based Systems, vol. 1, pp. 1-5.

Ting-Peng L. (2008) Recommendation systems for decision support: An editorial introduction, Decision Support Systems, vol.28, pp. 67-77.

Venugopal V. and Baets W. (1994) Neural networks and their applications in marketing management, Journal of Systems Management, vol. 45, no. 9, pp. 16-21.

Wanke P., Barros C. P. and Faria J. R. (2015) Financial distress drivers in Brazilian banks: A dynamic slacks approach, European Journal of Operational Research, vol. 240, pp. 258-268.

Zibanezhad E. and Foroghi M. D. (2011) Applying Decision Tree to Predict Bankruptcy. Computer Science and Automation Engineering (CSAE), IEEE International Conference, vol. 4, pp. 165-169.

Zopounidis C. and Dimitras A. I. (1998) Multicriteria decision aid methods for the prediction of business failure, Springer.