Stock market identification and estimation based on reduced fuzzy recursive least-squares approach

Stock market identification and forecasting are highly complex in terms of mathematical modeling due to the complexity of internal structure and external forces that contributes the nonlinear dynamics of its behaviors. In this paper, fuzzy system is used to identify the stock market and produce the parameters to estimate the closing price of the selected stock market. Due to the abilities of incorporating linguistic information, fuzzy system is proven to be a universal approximator at arbitrary accuracy. Here, fuzzy system with reduced fuzzy basis function is trained to be adaptive based on the recursive least-squares (RLS) approach. With the limitation of information available (black-box modeling), reduced fuzzy RLS approach is able to capture the nonlinear dynamics of the stock market and the simulation results are promising.