Hybrid neural network-based metaheuristics for prediction of financial markets: a case study on global gold market

Abstract Technical analysis indicators are popular tools in financial markets. These help investors to identify buy and sell signals with relatively large errors. The main goal of this study is develop new practical methods fake obtained from technical the precious metals market. In paper, we analyze these different ways based on recorded for 10 months. novelty research propose hybrid neural network-based metaheuristic algorithms analyzing them accurately while increasing performance indicators. We combine a convolutional network bidirectional gated recurrent unit whose hyperparameters optimized using firefly algorithm. To determine select most influential variables target variable, use another successful recently developed metaheuristic, namely, moth-flame optimization Finally, compare proposed models other state-of-the-art single deep learning machine literature. finding that metaheuristics can be useful as decision support tool address control enormous uncertainties