Forecasting stock market closing prices is a critical task for business intelligence, as it provides insights for financial planning and decision-making. The complexity arises due to diverse market patterns and various external factors that influence stock prices. This study presents a robust framework that incorporates market styles, economic indicators and news sentiments to enhance prediction power. The machine learning models are trained on aggregated datasets from companies exhibiting similar market styles. Experiments using Yahoo Finance data from five major companies (Apple, Amazon, Google, Meta, and Nike) show that accurate identification of market styles by the proposed model significantly improves pattern detection, while sentiment and economic indicators further enhance prediction quality. The findings demonstrate how multi-factor modeling can strengthen business intelligence systems by providing more reliable forecasts and supporting data-driven financial strategies.