Predicting Stock Market Time Series Using Evolutionary Artificial Neural Networks with Hurst Exponent Input Windows

@incollection{citeulike:2450129, abstract = {Predicting stock market time series is a challenging problem due to their random nature, non-stationarity and noise. In this study, we introduce an enhanced evolutionary artificial neural network (EANN) model to meet this challenge. Here, fractal analyses based on Hurst exponent calculations are used to characterize the time series and to identify appropriate input windows for the EANN. We investigate the efficacy of the model using closing price time series for a suite of stocks listed on the SPI index on the Australian Stock Exchange. The results show that Hurst exponent configured models out-perform basic EANN models in terms of average trading profit found using a simple trading strategy.}, author = {Selvaratnam, Somesh and Kirley, Michael }, citeulike-article-id = {2450129}, doi = {http://dx.doi.org/10.1007/11941439\_66}, journal = {AI 2006: Advances in Artificial Intelligence}, keywords = {ann, evoluation, predict, stock}, pages = {617--626}, posted-at = {2008-03-01 05:27:32}, priority = {2}, title = {Predicting Stock Market Time Series Using Evolutionary Artificial Neural Networks with Hurst Exponent Input Windows}, url = {http://dx.doi.org/10.1007/11941439\_66}, year = {2006} }

TY - CHAP ID - citeulike:2450129 L3 - citeulike-article-id:2450129 N2 - Predicting stock market time series is a challenging problem due to their random nature, non-stationarity and noise. In this study, we introduce an enhanced evolutionary artificial neural network (EANN) model to meet this challenge. Here, fractal analyses based on Hurst exponent calculations are used to characterize the time series and to identify appropriate input windows for the EANN. We investigate the efficacy of the model using closing price time series for a suite of stocks listed on the SPI index on the Australian Stock Exchange. The results show that Hurst exponent configured models out-perform basic EANN models in terms of average trading profit found using a simple trading strategy. JF - AI 2006: Advances in Artificial Intelligence EP - 626 TI - Predicting Stock Market Time Series Using Evolutionary Artificial Neural Networks with Hurst Exponent Input Windows SP - 617 KW - ann KW - evoluation KW - predict KW - stock AU - Selvaratnam, Somesh AU - Kirley, Michael PY - 2006/// UR - http://dx.doi.org/10.1007/11941439_66 ER -