Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange

During the recent decades, neural network models have been focused upon by researchers due to their more real performance and on this basis, different types of these models have been used in forecasting. Now, there is a question that which kind of these models has more explanatory power in forecasting the future processes of the stock. In line with this, the present paper made a comparison between static and dynamic neural network models in forecasting (uninvariable) the return of Tehran Stock Exchange (TSE) index in order to find the best model to be used for forecasting this series. The data were collected daily from 26/11/2009 to 17/10/2014. The models examined in this study included two static models (Adaptive Neuro-Fuzzy Inference Systems "ANFIS" and Multi-layer Feed-forward Neural Network "MFNN") and a dynamic model (nonlinear neural network autoregressive model "NNAR"). The findings showed that based on the Mean Square Error and Root Mean Square Error criteria, ANFIS model had a much higher forecasting ability compared to other models.

[1]  Seda Sahin,et al.  Hybrid expert systems: A survey of current approaches and applications , 2012, Expert Syst. Appl..

[2]  Reza Kamali,et al.  A comparison of neural networks and adaptive neuro-fuzzy inference systems for the prediction of water diffusion through carbon nanotubes , 2013 .

[3]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[4]  Daewon Kim,et al.  Are exchange rate movements predictable in Asia-Pacific markets? Evidence of random walk and martingale difference processes , 2012 .

[5]  Atila Dorum,et al.  Modelling the rainfall-runoff data of susurluk basin , 2010, Expert Syst. Appl..

[6]  Alaa F. Sheta,et al.  Time-series forecasting using GA-tuned radial basis functions , 2001, Inf. Sci..

[7]  Edward Leon. Armbrust.  Problems in least squares , 1965 .

[8]  Kurt Hornik,et al.  Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.

[10]  H. Markowitz,et al.  The Random Character of Stock Market Prices. , 1965 .

[11]  H. White.  Nonparametric Estimation of Conditional Quantiles Using Neural Networks , 1990 .

[12]  R. Dase,et al.  Application of Artificial Neural Network for stock market predictions: A review of literature , 2010 .

[13]  Eleftherios Giovanis.  Application of Feed-Forward Neural Networks Autoregressive Models with Genetic Algorithm in Gross Domestic Product Prediction , 2010 .

[14]  Roman Matkovskyy,et al.  Forecasting the Index of Financial Safety (IFS) of South Africa using neural networks , 2012 .

[15]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[16]  Yen-Ming Chiang,et al.  Comparison of static-feedforward and dynamic-feedback neural networks for rainfall -runoff modeling , 2004 .

[17]  Ah Chung Tsoi,et al.  STATIC AND DYNAMIC PREPROCESSING METHODS IN NEURAL NETWORKS , 1995 .

[18]  Sisira R. N. Colombage.  Financial markets and economic performances: Empirical evidence from five industrialized economies , 2009 .

[19]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[20]  Hao-Yun Kao,et al.  Leuconostoc Mesenteroides Growth in Food Products: Prediction and Sensitivity Analysis by Adaptive-Network-Based Fuzzy Inference Systems , 2013, PloS one.

[21]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[22]  Tugrul U. Daim,et al.  Using artificial neural network models in stock market index prediction , 2011, Expert Syst. Appl..

[23]  Kurt Hornik,et al.  Stationary and Integrated Autoregressive Neural Network Processes , 2000, Neural Computation.

[24]  Dimitrios I. Fotiadis,et al.  Automated fuzzy model generation through weight and fuzzification parameters’ optimization , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[25]  Jorg Bley.  Are GCC stock markets predictable , 2011 .

[26]  S. Raghavendra,et al.  Testing for Nonlinear Dependence in the Credit Default Swap Market , 2011 .

[27]  Tong-Seng Quah.  Using Neural Network for DJIA Stock Selection , 2007, Eng. Lett..

[28]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[29]  D. Sornette,et al.  Fundamental factors versus herding in the 2000–2005 US stock market and prediction , 2005, physics/0505079.

[30]  Amandeep Kaur,et al.  An introduction to neural network , 2016 .

[31]  David Zimbra,et al.  Medium term system load forecasting with a dynamic artificial neural network model , 2006 .

[32]  Maryam Ahmadifard,et al.  Forecasting stock market return using ANFIS : the case of Tehran Stock Exchange , 2013 .

[33]  Sneha Soni,et al.  Applications of ANNs in Stock Market Prediction : A Survey , 2011 .

[34]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[35]  Halbert White,et al.  On learning the derivatives of an unknown mapping with multilayer feedforward networks , 1992, Neural Networks.

[36]  E. Olmedo.  Is there chaos in the Spanish labour market , 2011 .

[37]  M. Thenmozhi,et al.  FORECASTING STOCK INDEX RETURNS USING NEURAL NETWORKS , 2022 .

[38]  B. LeBaron,et al.  A test for independence based on the correlation dimension , 1996 .

[39]  Pritam Radheshyam Charkha.  Stock Price Prediction and Trend Prediction Using Neural Networks , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.

[40]  David G. Loomis,et al.  Improving forecasting through textbooks — A 25 year review , 2006 .

[41]  C. Granger,et al.  Efficient Market Hypothesis and Forecasting , 2002 .

[42]  Halbert White,et al.  Using feedforward networks to distinguish multivariate populations , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[43]  Çagdas Hakan Aladag,et al.  Forecasting nonlinear time series with a hybrid methodology , 2009, Appl. Math. Lett..

[44]  Narendra Singh Raghuwanshi,et al.  Flood Forecasting Using ANN, Neuro-Fuzzy, and Neuro-GA Models , 2009 .

[45]  Huy Nguyen,et al.  A neural fuzzy approach to modeling the thermal behavior of power transformers , 2007 .

[46]  Ľuboš Briatka.  How Big is Big Enough? Justifying Results of the Iid Test Based on the Correlation Integral in the Non-Normal World , 2006 .

[47]  Geoffrey E. Hinton,et al.  The appeal of parallel distributed processing , 1986 .

[48]  B. LeBaron,et al.  Nonlinear Dynamics and Stock Returns , 2021, Cycles and Chaos in Economic Equilibrium.

[49]  Tugba Taskaya-Temizel,et al.  2005 Special Issue: A comparative study of autoregressive neural network hybrids , 2005 .

[50]  Kewei Hou,et al.  Market Frictions, Price Delay, and the Cross-Section of Expected Returns , 2003 .

[51]  Clive W. J. Granger,et al.  Testing for neglected nonlinearity in time series models: A comparison of neural network methods and alternative tests , 1993 .

[52]  Oscar Castillo,et al.  A new approach for time series prediction using ensembles of ANFIS models , 2012, Expert Syst. Appl..

[53]  Jiamei Deng,et al.  Dynamic neural networks with hybrid structures for nonlinear system identification , 2013, Eng. Appl. Artif. Intell..

[54]  Lotfi A. Zadeh,et al.  Please Scroll down for Article International Journal of General Systems Fuzzy Sets and Systems* Fuzzy Sets and Systems* , 2022 .

[55]  Feng Li,et al.  Application Study of BP Neural Network on Stock Market Prediction , 2009, 2009 Ninth International Conference on Hybrid Intelligent Systems.

[56]  A. Lo,et al.  The Size and Power of the Variance Ratio Test in Finite Samples: a Monte Carlo Investigation , 1988 .