Soft computing model coupled with statistical models to estimate future of stock market

Almost every organization around the globe is working with uncertainty due to inevitable changes and growth in every sphere of life. These changes affect directly or indirectly the stock market prices which makes forecasting a challenging task. So, the need for reliable, cost-effective, and accurate forecasting models significantly arises to reduce risk and uncertainty in stock market investment. Different time series models have been proposed by data scientists and researchers for accurate prediction of the future with the least errors. Econometric autoregressive time series models such as autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models have established forecasting models capable of generating accurate forecasts. Wavelet methods, being capable of handling nonlinear data, combined with autoregressive models generate more accurate forecasts. In this present study, soft computing models of discreet wavelet transformation and wavelet denoising combined with autoregressive models are developed to forecast the weekly and daily closing prices of the BSE100 S&P Sensex index. Statistical error analysis of the forecasting outcomes of coupled models has been made to evaluate the performance of the prediction of these models. The prediction results reveal that soft computing methods coupled with autoregressive models (wavelet-ARIMA and wavelet denoise-ARIMA) generate considerably accurate forecasts as compared to baseline models (simple regression, ARMA and ARIMA models) and coupled models (wavelet-ARMA and wavelet denoise-ARMA models).