The Comparison of Financial Crisis Prediction Strength of Different Artificial Intelligence Techniques
Subject Areas :
Zahra Pourzamani
1
(
استادیار دانشگاه آزاد اسلامی واحد تهران مرکزی
)
Hassan kalantari
2
(
کارشناس ارشد حسابداری دانشگاه آزاد اسلامی واحد تهران مرکزی
)
Keywords: Bankruptcy Prediction, Linear Genetic Algorithms, Nonlinear Genetic Algorithm, Neural network,
Abstract :
Rapid technological advances and vast environmental changes, leading to increasing competition and limit access to benefits and likely to suffer financial crisis has increased. Purpose of this study is investigating financial crisis prediction strength of different artificial intelligence techniques(linear and nonlinear genetic algorithm and neural network). Based on available information and statistics, of all companies listed in Tehran Stock Exchange, 72 companies have been subject to Article 141 trade law and 72 companies have not been subject to this Article was elected. Results of Mc-Nemar test for genetic algorithms techniques and neural network showed that there are not significant differences between linear and nonlinear genetic algorithms with neural network. Although the predictive accuracy of nonlinear genetic algorithm(90%) and linear genetic algorithms(80%) is more than of the neural network(70%) but this difference is not statistically significant.
سازمان بورس اوراق بهادار تهران - شورای بورس، (1378)،"مجموعه قوانین و آیین‌نامه‌های بورس اوراق بهادار"، ماده 75 و 76 انحلال شرکت‌ها، چاپ اول.
ساعی، رضا و سعید فلاح‌پور، (1387)، "کاربرد ماشین بردار پشتیبان در پیش‌بینی درماندگی مالی شرکت‌ها با استفاده از نسبت‌های مالی"، فصلنامه بررسی‌های حسابداری و حسابرسی،شماره 53، ص17-34.
فرج‌زاده دهکردی، حسن، (1384)،"کاربرد الگوریتم ژنتیک در الگوبندی پیش‌بینی ورشکستگی"، پایان‌نامه کارشناسی ارشد حسابداری، دانشکده علوم انسانی دانشگاه تربیت مدرس.
فقیه، نظام‌الدین، (1383)،"الگوریتم ژنتیک در برنامه‌ریزی بازرسی‌های پیش‌گیرانه"، شیراز، نسیم حیات.
کیارسی، آوا ، زهرا پورزمانی و افسانه توانگر، (1388)،"مقایسه کارایی دوروش رگرسیون لوجیت و تحلیل ممیزی چندمتغیره در تشخیص توانمندی مالی شرکت‌ها(در مورد شرکت‌های پذیرفته شده در بورس اوراق بهادار)"، پایان‌نامه کارشناسی ارشد، دانشکده اقتصاد و حسابداری دانشگاه آزاد اسلامی واحد تهران مرکزی.
منصور، جهانگیر، (1379)،"قانون تجارت همراه با قانون چک، آیین‌نامه اصلاحی ثبت تشکیلات و موسسات غیرتجاری"، نشر دیدار، چاپ هشتم.
نورالدین، مصطفی ، زهرا پورزمانی و رضا کی‌پور، (1389)،"بررسی و مقایسه توانمندی مدل‌های پیش‌بینی ورشکستگی(الگوهای مورد مطالعه مدل‌های مبتنی بر نسبت‌های مالی سنتی، الگوریتم ژنتیک و شبکه عصبی)"، پایان‌نامه کارشناسی ارشد، دانشکده اقتصاد و حسابداری دانشگاه آزاد اسلامی واحد تهران مرکزی.
Altman, E.I.,(1968) , "Financial Ratios, Discriminant Analysis and the Rrediction of Corporate Bankruptcy", Journal of Finance, 23,PP. 589–609.
Beaver, W.H.,(1966) , "Financial Ratios as Predictors of Failure", Journal of Accounting Research 4, Empirical Research in Accounting: ed Studies,PP. 71-111.
Beaver, W.H., McNichols, M.F. and Rhie, J.W.,(2005) , "Have Financial Statements Become Less Informative? Evidence the Ability of Financial Ratios to Predict Bankruptcy", Review of Accounting Studies, 10, ,PP. 93–122.
Etemadi, H., Rostamy, A., and Dehkordi, H.(2009) , "A Genetic Programming Model for Bankruptcy Prediction: Empirical Evidence Iran", Expert Systems with Applications, 36(2) ,PP. 3199–3207.
Galvao, R.K.,(2004) , "Ratio ion for Classification Models", Data Mining and Knowledge Discovery, 8,PP. 151–170.
Goldberg, D. E.,(1989) , "Genetic Algorithms in Search, Optimization and Machine Learning". New York: Addison-Wesley.
Gordon, M.J.,(1971) , "Towards Theory of Financial Distress". The Journal of Finance, 36,PP. 1347-56.
Haber, J.,(2006), "Theoretical Dvelopment of Bankruptcy Prediction Variables", the Journal of Theoretical Accounting Research, 2,PP. 82-101
Huang, S., Tsai, C.-F., Yen, D., and Cheng, Y.(2008) , "A Hybrid Financial Analysis Model for Business Failure Prediction", Expert Systems with Applications, 35(3) ,PP. 1034–1040.
Hung, C., and Chen, J.,(2009) ,"A ive Ensemble Based on Expected Probabilities for Bankruptcy Prediction", Expert Systems with Applications, 36(3) ,PP. 5297–5303.
Lin, R., Wang, Y. and Wu, C.,(2009) , "Developing a Business Failure Prediction Model via RST, GRA and CBR", Expert Systems with Applications, 36(2) ,PP. 1593–1600.
McKee, T.E. and Lensberg, T.(2002) , "Genetic Programming and Rough Sets: a Hybrid Approach to Bankruptcy Classification", European Journal of Operational Research, 138,PP. 436-51.
Min, J., and Jeong, C.,(2009) ,"A Binary Classification Method for Bankruptcy Prediction", Expert Systems with Applications, 36(3) ,PP. 5256–5263.
Min, J.H., and Lee, Y.C.,(2008) , "A Practical Approach to Credit Scoring", Expert Systems with Applications, 35(4) ,PP. 1762–1770.
Newton, G.W.,(1998) ,"Bankruptcy Insolvency Accounting Practice and Procedure", 1: Wiley,PP. 21-41
Odom, M. and Sharda, R.,(1993) ,"Neural Network for Bankruptcy Prediction, in: Trippi, Robert & Turban, Efrain, Neural Network in Finance and Investment: Using Artificial Intelligence to Improve Real- World Performance", Probus Publishing Company,PP. 177-185.
Ravi, V., and Pramodh, C.,(2008) , "Threshold Accepting Trained Principal Component Neural Network and Feature Subset ion: Application to Bankruptcy Prediction in Banks", Applied Soft Computing, 8(4) ,PP. 1539–1548.
Shah, J.R. and Murtaza, M.B.,(2000) , "A Neural Network Based Clustering Procedure for Bankruptcy Prediction", American Business Review, 18(2) ,PP. 80-86.
Shin, K. and Lee, Y.,(2002) , “A Genetic Algorithm Application in Bankruptcy Prediction Modeling”, Expert Systems with Applications, 23(3) ,PP. 321-8.
Sun, J., and Li, H.,(2008) , "Listed Companies Financial Distress Prediction Based on Weighted Majority Voting Combination of Multiple Classifiers", Expert Systems with Applications, 35(3) ,PP. 818–827.
Tam, K.Y., and Kiang, M.Y.,(1992) , "Managerial Applications of Neural Network: The Case of Bank Failure Predictions", Management Science, 38(7) ,PP. 926-947.
Tang, T.C. and Chi, L.C.,(2005) , " Neural Networks Analysis in Business Failure Prediction of Chinese Importers: A between-Countries Approach", Expert Systems with Applications, 29,PP. 244–255.
Tsai, C.F.,(2009) , "Feature ion in Bankruptcy Prediction", Knowledge-Based Systems, 22,PP. 120–127.
Whitaker, R.,(1999) , “The Early Stays of Financial Distress”, Journal of Economics and Finance, 23(2) ,PP. 122-133.
Wu, W.W.,(2010) , "Beyond Business Failure Prediction", Expert Systems with Applications, 37(3) ,PP. 2371–2376.
Zhang, G., HU, M.Y., Patuwo, B.E., and Indro, D.C.,(1999) , "Artificial Neural Network in Bankruptcy Prediction: General Framework and Cross-Validation Analysis", European Journal of Operational Research, 116(1), ,PP. 16-32.
