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  • Neural Networks Approach to the Random Walk Dilemma of Financial Time Series

    Author(s)
    Sitte, Renate
    Sitte, Joaquin
    Griffith University Author(s)
    Sitte, Renate
    Year published
    2002
    Metadata
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    Abstract
    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 ...
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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.
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    Journal Title
    Applied Intelligence
    Volume
    16
    DOI
    https://doi.org/10.1023/A:1014380315182
    Subject
    Artificial Intelligence and Image Processing
    Publication URI
    http://hdl.handle.net/10072/6634
    Collection
    • Journal articles

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