Comparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market

In this study, a new methodology is proposed to automatically determine six parameters of artificial neural network using population-based metaheuristics. We considered following three issues: What is the effect of used metaheuristic on performance? Which parameters are mostly selected? Is there a difference between the forecasting results when using stationary or nonstationary dataset that are selected according to the augmented Dickey-Fuller test statistics? Based upon results of performance measures, proposed method leads to significant opportunities to forecast stock market more effectively. We also expect proposed methodology can provide remarkable advantages for other complex, dynamic, and nonlinear forecasting problems.