[1] Abbasi E., & Abouec, A. (2008). Stock price forecast by using neuro-fuzzy inference system. Proceedings of World Academy of Science, Engineering and Technology, 36, 320–323. bonds. Journal of Financial Economics, 33, 3–56
[2] Bollerslev T.(1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Economics,; 31: 307-327.
[3] Bouchad J. P., Matacz A. (2001). Potters M. The Leverage Effect in Financial Markets: Retarded Volatility and Panic. Physical Review Letters, 87(22) : 228701-4.
[4] Box, G.P.F; Jenkins, G.M. (1978). Time Series Analysis: Forecasting and Control. 3rd Edn., Holden Day, San Francisco.
[5] Brock, W. A., W. Dechert, & J. Scheinkman. (1987). A test for independence based on the correlation dimension. Working paper, University of Winconsin at Madison, University of Houston, and University of Chicago.
[6] Chavoshi, K. (2001). The Behavior of Stock Prices in the Tehran Stock Exchange, M.S. thesis, The University of Imam Sadegh. (in Persian)
[7] Diebold FX, and Mariano RS. (2002). Comparing predictive accuacy. Journal of Business and Economic Statistics 20, 134-144
[8] Dunis C.L., J. Jalilov (2001). Neural Network Regression and Alternative Forcasting Techniques for Predicting Variables. Working Paper, Liverpool Busness School and CIBEF.
[9] Engle F. R. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 987-1007.
[10] Fama E., & French, K. (1993). Common risk factors in the returns on stocks and
[11] Haykin S. (1994), Neural Networks: a Comprehensive Foundation. Macmillan, New York.
[12] Jang J. S. R. (1993). ANFIS Adaptive network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics, 23, 665–685.
[13] Jang J.S.R., Sun C.T, Mizutani E. (1997) Neuro-Fuzzy and Soft Computing A Computional Approach to Learning and Machine Intelligence, Prentice Hall, 640pp.
[14] Johnston k., & Scott, E. (2000). GARCH Models and the Stochastic Process Underlying Exchange Rate Price Change. Journal of Financial and Strategic Decisions, Vol. 2. pp. 13-24.
[15] Kanas A. & Yannopoulous, A. (2001). Comparing linear and nonlinear forcasts for stock returns. International Review of Economics and finance, Vol. 10, pp 383-398
[16] Karyl Q.C, et al (2005). A Comparision between Fama and Frenchs Model and Artificial Neural Network in Predicating the Chinese stock market. Computer and operations research, vol 32, pp 2499-2512
[17] Kiink, T. Vesterstroem J. S. & Riget J. (2002). Particle swarm optimization with spatial particle extension, Proceedings of the lEEE Congress on Evolutionary Computation, , pp. 1474-1479.
[18] MATLAB user's guide, (2010). Fuzzy logic Toolbox, by the math works Inc.
[19] McMillan, D. G. (2001). Non-linear predictability of Stock market returns.international Review of Economics and Finance, 10, 353-368.
[20] Menhaj, M. (2000). Foundations of Artificial Intelligence, Volume 1 and 2, Amirkabir University of Technology Publishing. (in Persian)
[21] Monajemi, A., Abzari, M., & Shavazi, A. (2009). Stock price forecast in stock market using fuzzy neural network and genetic algorithm and the comparison with artificial neural network, Quarterly Journal of quantitative Economics, No. 3, pp 1-26. (in Persian)
[22] Moshiri, S., & Morovat, H. (2006). TEPIX forecast using linear and non-linear models, Quarterly Journal of Business, No. 41, pp 245-275. (in Persian)
[23] Roger J. S. Jang, C. T. Sun and Mizutani E. (1996). Neuro-Fuzzy and Soft Computing: a computational approach to learning and machine intelligence, to be published by Prentice-Hall
[24] Souri, A. (2011). Econometrics with Eviews 7 application, 2nd publishing, Tehran: Frahang Shenasi Publishing. (in Persian)
[25] Srinivasan K., Fisher D. (1995). Machine Learning Approaches to Estimating Software Development Effort. IEEE Transactions on Software Engineering. 21(2): 126–137.
[26] Takagi, T. & Sugeno, M. (1983). Derivation of fuzzy control rules from human operator’s control actions. In Proc. IFAC symp. Fuzzy Inform. Knowledge representation and decision analysis (pp. 55–60).
[27] Yim, Juliana. (2002). A comparison of neural networks with time series models for forcasting returns on a stock market index. Working paper, school of Economics and Finance.
[28] Yudong, Z., & Lenan, W. (2009). Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Systems with Applications, 36(5), 8849–8854.
