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  • 1

    1393 / 23 / 6

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  • 11

    1393 / 23

    kY

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    MAE RMSE MSE. n

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    R2

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    )MAE,MSE,RMSE,R2 (

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    - -2

    DATA TEST ANFIS MAE 00784/0 RMSE 013/0 MSE 000217/0 R2 98/0

    DATA TEST GMDH MAE 028/0 RMSE 038/0 MSE 0015/0 R2 89/0

    DATA TEST NEURAL MAE 035/0 RMSE 082/0 MSE 0067/0 R2 85/0

    :

    - -1

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  • 13

    1393 / 23

    ) 1997 (.

    .

    -

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    GMDH MLP

    .

    :

    )1

    .

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    .96-77 1 "

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    .

    4) Abarbanell, J.S., & Bushee, B.J.(1997). Fundamental Analysis, Future Earnings, and Stock

    Prices. Journal of Accounting Research, 35(1),PP. 1-24 . 5) Atashkari, K., Nariman-Zadeh, N., Glc, M., Khalkhali, A., & Jamali, A. (2007). Modelling

    and Multi-Objectiveoptimization of a Variable Valve-Timing Spark-ignition Engine Using

    Polynomial Neural Networks and Evolutionary Algorithms. Energy Conversion and

    Management, 48(3),PP. 1029-1041 . 6) Atsalakis, G.S., Dimitrakakis, E.M., & Zopounidis, C.D. (2011). Elliott Wave Theory and

    Neuro-Fuzzy Systems, in Stock Market Prediction: The WASP system. Expert Systems With

    Applications, 38(8), PP.9196-9206 . 7) Callen, J.L., Kwan, C.C.Y., Yip, P.C.Y., & Yuan, Y. (1996). Neural Network Forecasting of

    Quarterly Accounting Earnings. International Journal of Forecasting, 12(4),PP. 475-482 .

  • - EPS 14

    1393 / 23

    8) Cao, Q., & Gan, Q. (2009). Forecasting EPS of Chinese Listed Companies Using Neural

    Network with Genetic Algorithm . 9) Cheng, C.H., Hsu, J.W., & Huang, S.F. (2009). Forecasting Electronic Industry EPS Usingan

    Integrated ANFIS Model. Paper Presented at the Systems, Man and Cybernetics, 2009. SMC

    2009. IEEE International Conference on. 10) Graham, B. (1934). Security Analysis. School of Business, Columbia University . 11) Ivakhnenko, AG, & Mller, J.A. (1995). Present State and new Problems of Further GMDH

    Development. Systems Analysis Modelling Simulation, 20(1-2),PP. 3-16 . 12) Khashei, Mehdi, & Bijari, Mehdi. (2010). A Novel Hybridization of Artificial Neural

    Networks and ARIMA Models for Time Series Forecasting. Applied Soft Computing . 13) Khatibi, R., Ghorbani, M.A., Kashani, M.H., & Kisi, O. (2011). Comparison of Three

    Artificial Intelligence Techniques for Discharge Routing. Journal of Hydrology, 403(3),PP.

    201-212 . 14) Lev, B., & Thiagarajan, S.R. (1993). Fundamental Information Analysis. Journal of

    Accounting Research,PP. 190-215 . 15) Lubis, H.Y., & Director-Donnell, M. (2000). Initial Public Offering Prediction using Neural

    Network: The George Washington University. 16) Madala, H.R., & Ivakhnenko, A.G. (1994). Inductive Learning Algorithms for Complex

    Systems Modeling: CRC Press Boca Raton, FL. 17) Malinowski, P., & Ziembicki, P. (2006). Analysis of District Heating Network Monitoring by

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    28 . 18) Mantas ,CJ, Puche, JM, & Mantas, JM. (2006). Extraction of Similarity basedFuzzy rules

    from Artificial Neural Networks. International Journal of Approximate Reasoning, 43(2),PP.

    202-221 . 19) McCleary, R., & Hay, R.A. (1980). Applied Time Series Analysis for The Social Sciences:

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    Developed ANN and ANFIS models in Prediction of Carbon Monoxide Daily Concentration.

    Atmospheric Environment, 44,PP.476-482

    21) Patterson, Dan W. (1998). Artificial Neural Networks: Theory and Applications: Prentice Hall

    PTR. 22) Sumathi, Sai. (2009). Computational Intelligence Paradigms: Theory & Applications Using

    MATLAB: CRC. 23) Thomas, J. K. and Zhang, H. (2002). Inventory Changes and Future Returns Review of

    Accounting Studies. No. 7, PP.163-187

    24) Takagi, T., & Sugeno, M. (1985). Fuzzy Identification of Systems and its Applications to

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    Model. Neurocomputing, 50,PP.159-175.

    1. Egeli et al 2 . Cheng et al 3 .Zhang, G.and Michael ,Y,HU 4 . Lisa, F. and Rosa A,Schiavo 5 . Leung,M. and An Sing Chen and Hazem Daouk

  • 15

    1393 / 23

    6 . Qi, M. and Yangru , Wu 7 . G.F, Fllareiov & E.O. Averehenkow 8. Cao & Gan 9. Virli & Firisilin 10 . Multi Layer Perceptron 11 . Paterson et al 12 . Malinowski et al 13 . Sumathi 14 . Khashei 15 . Mantas et al 16 . Zhang 17 . Group Method of Data Handling

    19 . Madala 20 .Singular Value Decomposition 21 . Solving Normal Equation 22 . Ivakhnenko & Mller

  • 17

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  • 31

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    NI 002/0 000/0 000/0 142/0 134/0 860/0

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    1000 1000 1000 1000

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  • 32

    1393 / 23

    -12

    . 8 .

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    Sg 010/0 198/0 843/0 011/0 211/0 833/0 029/0 556/0 578/0

    Size NI 403/0- 425/1- 155/0 338/0- 202/1- 230/0 335/0- 191/1- 234/0

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    25. Jiambalvo 26. Rees 27. Teoh 28. Shiue & Lin 29. Yoon 30. Islam 31. Algharaballi & Albuloushi 32. Plummer & Mest 33. Marquardt & Wiedman 34. Stubben 35. Dechow & Dichev 36. McNichols 37. Watts & Zimmerman 38. Habib 39. Moses 40. Beatty 41. Matsumoto 42. Ndubizu & Tsetsekos 43. Dhaliwal 44. Dye 45. Ohlson 46. Value Relevance 47. Francis & Schipper 48. Barth & Beaver & Landsman

  • 41

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    14) Wakefield, R. l, (2008), Accounting and Machiavellianism, Behavioral Research in

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    ) ( . ) . () (

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    KZ2,3!4 15/0 005/0 053/0 84/2 0004/0 81/0 002/0 2/0

    Adjust R9 004/0 04/0

    123 123

    1230 1230

    :

  • 92

    1393 / 23

    CAPEX2,3 A2,3!4 : 5

    - 5

    #, !" $%&, $%&, !" ($$0, !" (#, !" 56, !" '&(, !"

    -e00/1 07-e64/5- 48/0- 85/2- 15/0 54/1-06 069/0

    P>|t | 01/0 077/0 236/0 619/0 502/0 005/0 003/0

    -t 61/2 78/1 19/1- 5/0- 67/0- 84/2 47/1

    123 123 123 123 123 123 123

    1230 1230 1230 1230 1230 1230 1230

    Prob >= F 0000/0 0000/0 0000/0 0000/0 0000/0 0000/0 0000/0

    e61/5 07-e73/4 95/0 2/4 053/0 25/0-07 026/0

    :

    LTI2,3 A2,3!4

    : 6

    - 6

    "! ,)&' "! ,56 "! ,#) "! ,0$$) "! ,&%$ ,&%$ "! ,#

    e18/2- 09-e07/1- 1092/0 0017/0- 0004/0 13/2-09 008/0

    P>|t | 494/0 194/0 787/0 01/0 878/0 81/0 464/0

    t 68/0 42/1- 27/0- 56/2 15/0- 24/0 73/0

    123 123 123 123 123 123 123

    1230 1230 1230 1230 1230 1230 1230

    Prob > F 6937/0 6937/0 6937/0 6937/0 6937/0 6937/0 6937/0

    e96/3 426/0 011/0 015/0 057/0-09 0014/0 0012/0

    :

  • 93

    1393 / 23

    -9-1

    5

    : . Prob > F = 0.0000

    :

    .

    :H0 ) (

    :H0 ) (

    Prob>chi2 = 0.3497

    . Prob>chi2 05. 0 >

    .

    6 Prob > F = 0.6937

    . Prob> F= 0.6937

    .

    -9-2

    -9-2-1

    . . ()

    .

    .

    .

  • 94

    1393 / 23

    : -9-2-2

    . . ()

    .

    .

    .

    -10

    .

    1381-1390 ) (

    .

    .

    KZ

    . 1384

    .

    .

    .

    -

    .

  • 95

    1393 / 23

    ") 1385 ( )1

    "

    . 45

    " ") 1385 ( )2

    .

    " ") 1385 ( )3

    . 8-12 52

    ") 1387 ( )4

    " ( )

    : .

    " ") 1390 ( )5

    .104-105 12 1390

    ") 1391 ( )6

    "

    :

    .

    ") 1378 ( )7

    : "

    .

    ") 1385 ( )8

    45 "

    45.-68

    ") 1389 ( )9

    113.-124 3 "

    ") 1388 ( )10

    3/57 "

    170-147.

  • 96

    1393 / 23

    ") 1389 ( )11

    "

    .1-22 6 12) Biddle , Gary and Gilles Hillary , (2006) , Accounting Quality and Firm-level Capital

    Investment. avilabel in www.ssrn.com .PP. 1-42

    13) Chen ,Zhihong ,(2005),Earning Quality A Thesis Submitted to The Hong Kong

    University of Science and for Degree of Doctor of Philosophy.PP. 1-45 14) Francis,J.Lafond,R.Olson,P.M.and shipper , K, (2004) ,Cost of Equity and Earnings

    attributes The accounting Reiview . Vol.79 , no.4,PP.294-309 15) Hugland, Henrik, (2010), Detecting Earning Management Using Neural Networks

    .Helsinki, Finland. Hanken School of economics, Department Of Accounting And Commercial Laws.

    16) Ke Li. Kevin,Wei Tang, ,Vicki, (2008),Earning Quality And Future Capital Investment :

    Evidence From Discretionary Accruals ,March 2008 ,Californai And Georgetown University.

    17) Liang , P. and Xiaoyan Wen, (2007) , Accounting Measurement Basis , Market Mispricing,

    and Firm Investment Efficiency, Journal of Accounting Research 45(1), PP. 155-197

    18) Richardson, S Sloan, R. & Soliman, M.T 2001. Information in Accruals about the Quality of

    Earnings. University of Michigan Business School from http://www.ebsco.com 19) Wei ,K.C .John.and Feixue Xie,(2005),Earnings Management ,Corporate Investment, and

    Stock Returns . School Business and Management , Hong Kong University of Science and Technology

    1. Discretionary Accruals

    2. Non-Discretionary Accruals

    3. Total Accruals

    4. Securities and Exchange Commission

    5. Hugland

    6. Healy Models

    7. De Angelo Models

    8. Jones Models

    9. Sloan Models

    10. Jeter and Shivakumar models

    11. Dechow and Dechev models

    12. Francis et al models

    13. Aboody , Hughes and Liu models

    14. Ke Li and Wei Tang models

    15. ke Li and Wei Tang

    16. Liang and Wen

    17. Biddle and Hilary

    18. Wei and Xie

    19. Bushman and Smith

    20. Total Working Capital Accrual

    21. Accrual Quality

    22. Gross Value of Property, Plant and Equipment

  • 97

    1393 / 23

    23.

    Capital Expenditure 24.

    Previous Total Assests 25.

    Total Accruals 26.

    Non-Discretionary Accruals 27.

    Lmount ,Polk and Saa-Reguejo

  • 99

    1393 / 23 / 6

    115 99

    1

    2

    3

    :

    1389 1385 90

    .

    .

    .

    .