U.S.A. S&P 500 stock market dynamism exploration with moving window and artificial intelligence approach

We propose an approach of artificial immune algorithm, fuzzy theorem, support vector regression, and seasonal moving window to explore stock dynamism among same seasons in continuous years for USA S&P 500 stock indexes. First, we select optimal number of trading days to calculate technical indicator values. We apply artificial immune algorithm to locate optimal combination of technical indicators as input variables. The property of nonlinearity and high dimensionality of the support vector regression is employed to explore the stock price patterns.