  Prediction of stock prices plays a significant role in aiding the
decision-making of investors. Considering its importance, a growing literature
has emerged trying to forecast stock prices with improved accuracy. In this
study, we introduce an innovative approach for forecasting stock prices with
greater accuracy. We incorporate external economic environment-related
information along with stock prices. In our novel approach, we improve the
performance of stock price prediction by taking into account variations due to
future expected macroeconomic policy changes as investors adjust their current
behavior ahead of time based on expected future macroeconomic policy changes.
Furthermore, we incorporate macroeconomic variables along with historical stock
prices to make predictions. Results from this strongly support the inclusion of
future economic policy changes along with current macroeconomic information. We
confirm the supremacy of our method over the conventional approach using
several tree-based machine-learning algorithms. Results are strongly conclusive
across various machine learning models. Our preferred model outperforms the
conventional approach with an RMSE value of 1.61 compared to an RMSE value of
1.75 from the conventional approach.
