Time series: theory and methods
Time series: theory and methods
Neural Networks in the Capital Markets
Neural Networks in the Capital Markets
Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets
Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets
Analysis of the predictive ability of time delay neural networksapplied to the S&P 500 time series
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Time series forecasting with Qubit Neural Networks
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
Combining artificial neural network and particle swarm system for time series forecasting
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A hybrid method for tuning neural network for financial time series forecasting
MS '08 Proceedings of the 19th IASTED International Conference on Modelling and Simulation
A Hybrid Intelligent Morphological Approach for Stock Market Forecasting
Neural Processing Letters
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A class of hybrid morphological perceptrons with application in time series forecasting
Knowledge-Based Systems
An evolutionary approach to design dilation-erosion perceptrons for stock market indices forecasting
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A robust automatic phase-adjustment method for financial forecasting
Knowledge-Based Systems
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
A dilation-erosion-linear perceptron for bovespa index prediction
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
A hybrid model for s&p500 index forecasting
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Information Systems Frontiers
A Morphological-Rank-Linear evolutionary method for stock market prediction
Information Sciences: an International Journal
Evolutionary Learning Processes to Design the Dilation-Erosion Perceptron for Weather Forecasting
Neural Processing Letters
Correcting and combining time series forecasters
Neural Networks
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Predictions of financial time series often show a characteristic one step shift relative to the original data as in a random walk. This has been the cause for opposing views whether such time series do contain information that can be extracted for predictions, or are simply random walks. In this case study, we show that NNs that are capable of extracting weak low frequency periodic signals buried in a strong high frequency signal, consistently predict the next value in the series to be the current value, as in a random walk, when used for one-step-ahead predictions of the detrended S&P 500 time series. In particular for the Time Delay Feed Forward Networks and Elman Networks of various configurations, our study supports the view of the detrended S&P 500 being a random walk series. This is consistent with the long standing hypothesis that some financial time series are random walk series.