Stock trend prediction using neurofuzzy predictors based on brain emotional learning algorithm

Abstract

Short term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. To predict stock trends, we exploit Emotional Learning Based Fuzzy Inference System (ELFIS). ELFIS has the advantage of low computational complexity in comparison with other multi-objective optimization methods. The performance of ELFIS in the prediction of stock prices will be compared with that of Adaptive Network Based Fuzzy Inference System (ANFIS). Simulations show better performance for ELFIS.