Development of an ensemble learning-based intelligent model for stock market forecasting

The use of articial intelligence-based models has shown that the market is predictable despite its uncertainty and unstable nature. The most important challenge of the proposed models in the stock market is to ensure high accuracy of results and high forecasting eciency. Another challenge, which is a prerequisite for making decisions and using the results of the forecast for protability of transactions, is to forecast the trend of stock price movements in forecasting price targets. To overcome the mentioned challenges, this paper employs Ensemble Learning (EL) model using intelligence-based learners and metaheuristic optimization methods to maximize the improvement of forecasting performance. In addition, to take into account the direction of price changes in stock price forecasting, a two-stage structure is used. In the rst stage, the next movement of the stock price (increase or decrease) is forecasted and its outcome is then employed to forecast the price in the second stage. In both stages, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to optimize the aggregation results of the base learners. The evaluation results of stock market dataset show that the proposed model has higher accuracy than other models used in the literature.