This study presents a Business Intelligence (BI) approach to forecast daily changes in 27 stocksÕ prices from 8 industries. The BI approach uses a financial data mining technique specifically Neural Network to assess the feasibility of financial forecasting compared to regression model using ordinary least squares estimation method. We used eight indicators such as macroeconomic indicators, microeconomic indicators, political indicators, market indicators, market sentiment indicators, institutional investor, business cycles, and calendar anomaly to predict changes in stocksÕ prices. The results shows NN model better predicts stock prices with up to 92%% of forecasting accuracy.