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

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استاد مدیریت فناوری اطلاعات، دانشکده مدیریت و حسابداری، دانشگاه تهران، ایران

2 استادیار مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران، ایران

3 کارشناس ارشد، مدیریت فناوری اطلاعات، دانشکده مدیریت، دانشگاه تهران، تهران، ایران

چکیده

با ظهور انواع جدید کسب‌وکارها که منتج به افزایش پیچیدگی در فضای کسب‌وکار شده است، مدیران و سرمایه‌گذاران بیش‌ازپیش نیازمند ابزارهایی هستند که با استفاده از آن‌ها، شفافیت بیشتری در وضعیت آتی کسب‌وکار خود ایجاد نمایند. وضعیت مالی سازمان‌ها در همه ادوار از اهمیت ویژه‌ای برخوردار بوده است و بررسی سودآوری کسب‌وکار نیز از طریق تحلیل وضعیت مالی سازمان تبیین می‌شود. صورت‌های مالی، وضعیت مالی سازمان را در یک دوره مشخص در بردارند. در این تحقیق سعی بر آن است که با استفاده از نسبت‌های مالی و با به‌کارگیری الگوریتم‌های داده‌کاوی، سیستمی طراحی شود که با توجه به عملکرد گذشته شرکت‌های صنعتی، سود خالص آن‌ها را در آینده پیش‌بینی نماید و بر مبنای آن، تحلیل مناسبی از وضعیت عملکردی شرکت حاصل گردد. سیستم مبتنی بر شبکه عصبی که در این تحقیق طراحی شده است، با کشف روابط موجود میان نسبت‌های مالی و سودآوری شرکت‌ها، اقدام به پیش­بینی سود خالص سازمان‌ها می‌نماید.

کلیدواژه‌ها


عنوان مقاله [English]

Financial Performance Prediction System in Industrial Companies through Data Mining Algorithms

نویسندگان [English]

  • Babak Sohrabi 1
  • Iman Raeesi Vanani 2
  • Babak Bootorabi 3
1 Professor, Faculty of Management, University of Tehran, Tehran, Iran
2  Assistant Professor, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran.
3 MSc of Information Technology Management, University of Tehran, Tehran, Iran
چکیده [English]

 
With the emergence of new businesses leading to the complicated and changing business environments, industrial managers and investors need tools and mechanisms to acquire a more clarified view of their business in different financial aspects in the future. The financial status of industrial firms has always had a significant analytical role and the evaluation of profitability has been conducted through the analysis of financial indicators that appear as key performance measures. In this regard, financial statements provides the stakeholders with accurate organizational status in a specific period of time. In the current research, the researchers have attempted to utilize the financial ratios as well as data mining algorithms so as to design a system that accurately predicts the net profit based on the previous performance of firms and accordingly, providing an appropriate performance analysis. The designed neural network model predicts the profit through the detection of relationships among financial ratios and previous profitability of the industrial firm.

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 (6th 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 (6th 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 (6th 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.