Applying artificial immune algorithm to explore the seasonal effect in the stock market

The cyclic time effect, such as January effect or weekend effect, is well documented in literature. Therefore, it is very important to consider the seasonality dynamism. We propose a hybrid approach of artificial immune algorithm, support vector regression, and seasonal moving window to explore stock quarterly seasonality dynamism among same seasons in continuous years.