B. Qian and K. Rasheed (USA)
Hurst exponent, time series analysis, neural networks, Monte Carlo simulation, forecasting
The Hurst exponent (H) is a statistical measure used to classify time series. H=0.5 indicates a random series while H>0.5 indicates a trend reinforcing series. The larger the H value is, the stronger the trend. In this paper we investigate the use of the Hurst exponent to classify series of financial data representing different periods of time. Experiments with backpropagation Neural Networks show that series with large Hurst exponent can be predicted more accurately than those with H value close to 0.50. Thus the Hurst exponent provides a measure for predictability.
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