Seasonality effect of stock dynamism

We propose a hybrid approach of seasonal moving window, genetic algorithm, and support vector regression to explore seasonality effect for the stock indexes in two developed markets. First, we utilize genetic algorithm to locate the approximate optimal combination of technical indicators. Then the property of nonlinearity and high dimensionality of the support vector regression is employed to explore the stock price patterns. Finally, we adopt seasonal moving window to capture the seasonality effect of stock market returns. We find that the proposed method outperforms buy-and-hold returns.