A Hybrid Intelligent Morphological Approach for Stock Market Forecasting

In this paper, a hybrid intelligent morphological approach is presented for stock market forecasting. It consists of a hybrid intelligent model composed of a Modular Morphological Neural Network (MMNN) and a Modified Genetic Algorithm (MGA), which searches for the minimum number of time lags for a correct time series representation, as well as by the initial weights, architecture and number of modules of the MMNN. Each element of the MGA population is trained via Back Propagation (BP) algorithm to further improve the parameters supplied by the MGA. Initially, the proposed method chooses the most tuned prediction model for time series representation, then it performs a behavioral statistical test in the attempt to adjust time phase distortions that appear in financial time series. An experimental analysis is conducted with the proposed method using four real world time series and five well-known performance measurements, demonstrating consistent better performance of this kind of morphological system.

[1]  Alireza Khotanzad,et al.  Combination of artificial neural-network forecasters for prediction of natural gas consumption , 2000, IEEE Trans. Neural Networks Learn. Syst..

[2]  T. Mills,et al.  The Econometric Modelling of Financial Time Series. , 1995 .

[3]  N. P. Landsman,et al.  A random walk down Wall Street , 2008 .

[4]  Elmar Steurer,et al.  Much ado about nothing? Exchange rate forecasting: Neural networks vs. linear models using monthly and weekly data , 1996, Neurocomputing.

[5]  Marko Hocevar,et al.  Prediction of cavitation vortex dynamics in the draft tube of a francis turbine using radial basis neural networks , 2004, Neural Computing & Applications.

[6]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[7]  Michael P. Clements,et al.  Forecasting economic and financial time-series with non-linear models , 2004 .

[8]  F.H.F. Leung,et al.  Tuning of the structure and parameters of neural network using an improved genetic algorithm , 2001, IECON'01. 27th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.37243).

[9]  T. Rao,et al.  An Introduction to Bispectral Analysis and Bilinear Time Series Models , 1984 .

[10]  T. Ozaki.  2 Non-linear time series models and dynamical systems , 1985 .

[11]  Renate Sitte,et al.  Neural Networks Approach to the Random Walk Dilemma of Financial Time Series , 2002, Applied Intelligence.

[12]  G. Matheron.  Random Sets and Integral Geometry , 1976 .

[13]  James C. Felmley.  The Emerging Forecasting Process at Reckitt & Colman Inc , 1992 .

[14]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[15]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[16]  H. Kantz,et al.  Nonlinear time series analysis , 1997 .

[17]  Petros Maragos,et al.  MRL-filters: a general class of nonlinear systems and their optimal design for image processing , 1998, IEEE Trans. Image Process..

[18]  A. Walden,et al.  Spectral analysis for physical applications : multitaper and conventional univariate techniques , 1996 .

[19]  Guoqiang Peter Zhang,et al.  Quarterly Time-Series Forecasting With Neural Networks , 2007, IEEE Transactions on Neural Networks.

[20]  Marie Cottrell,et al.  Neural modeling for time series: A statistical stepwise method for weight elimination , 1995, IEEE Trans. Neural Networks.

[21]  Tiago A. E. Ferreira,et al.  An Evolutionary Morphological Approach for Financial Time Series Forecasting , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[22]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[23]  Michael P. Clements,et al.  On the limitations of comparing mean square forecast errors , 1993 .

[24]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[25]  Tiago Alessandro Espínola Ferreira,et al.  An Intelligent Hybrid Approach for Designing Increasing Translation Invariant Morphological Operators for Time Series Forecasting , 2007, ISNN.

[26]  Lutz Prechelt,et al.  PROBEN 1 - a set of benchmarks and benchmarking rules for neural network training algorithms , 1994 .

[27]  Jingtao Yao,et al.  A case study on using neural networks to perform technical forecasting of forex , 2000, Neurocomputing.

[28]  Tiago Alessandro Espínola Ferreira,et al.  A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks , 2008, Neural Processing Letters.

[29]  Arie Preminger,et al.  Forecasting Exchange Rates: A Robust Regression Approach , 2005 .

[30]  T. Myhre.  Financial Forecasting at Martin Marietta Energy Systems, Inc , 1992 .

[31]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[32]  M. B. Priestley,et al.  Non-linear and non-stationary time series analysis , 1990 .