Integration of genetic algorithm with artificial neural network for stock market forecasting

Traditional statistical as well artificial intelligence techniques are widely used for stock market forecasting. Due to the nonlinearity in data, a model developed using traditional or single intelligent technique may not accurately forecast results. Therefore, there is need develop hybridization of an effective predictive model. In this study, we propose forecasting method based on hybrid Artificial Neural Network (ANN) and Genetic Algorithm (GA) uses two US indices, DOW30 NASDAQ100, The data were partitioned into training, testing, validation datasets. was done COVID-19 period. experimental findings obtained NASDAQ100 reveal that accuracy GA ANN greater than (BPANN) technique, both short long term.