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طراحی سیستم پیشبینی کننده عملکرد مالی در شرکتهای صنعتی بر مبنای روشهای دادهکاوی | ||
مطالعات مدیریت کسب و کار هوشمند | ||
مقاله 1، دوره 4، شماره 14، اسفند 1394، صفحه 1-21 اصل مقاله (794.8 K) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22054/ims.2016.5146 | ||
نویسندگان | ||
بابک سهرابی1؛ ایمان رئیسی وانانی* 2؛ بابک بوترابی3 | ||
1استاد مدیریت فناوری اطلاعات، دانشکده مدیریت و حسابداری، دانشگاه تهران، ایران | ||
2استادیار مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران، ایران | ||
3کارشناس 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 | ||
عملکرد مالی؛ شرکتهای صنعتی؛ دادهکاوی؛ شبکههای عصبی؛ اعتبارسنجی | ||
مراجع | ||
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.
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