An innovative recurrent error-based neuro-fuzzy system with momentum for stock price prediction

Neuro-fuzzy system is now one of the most widely used tools in the field of artificial intelligence systems. This study proposes a novel approach for time series stock market price prediction using a recurrent error-based neuro-fuzzy system with momentum (RENFSM). The basic idea of this approach is to use time series price momentum and time series prediction error adjusted to the well-known adaptive neuro-fuzzy inference system, ANFIS. Extended from ANFIS, the aim of this study is to propose a reliable prediction system with minimal error. Moreover, to evaluate the proposed model strength, four top-listed stocks from Dhaka stock exchange were applied. In the experiments, several choices of momentum from 3 to 20 days are selected for data preprocessing. It was found that the proposed RENFSM performed superiorly and was more reliable compared to the existing methods such as ANFIS and neural networks.