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Prediction of stock prices plays a significant role in aiding thedecision-making of investors. Considering its importance, a growing literaturehas emerged trying to forecast stock prices with improved accuracy. In thisstudy, we introduce an innovative approach for forecasting stock prices withgreater accuracy. We incorporate external economic environment-relatedinformation along with stock prices. In our novel approach, we improve theperformance of stock price prediction by taking into account variations due tofuture expected macroeconomic policy changes as investors adjust their currentbehavior ahead of time based on expected future macroeconomic policy changes.Furthermore, we incorporate macroeconomic variables along with historical stockprices to make predictions. Results from this strongly support the inclusion offuture economic policy changes along with current macroeconomic information. Weconfirm the supremacy of our method over the conventional approach usingseveral tree-based machine-learning algorithms. Results are strongly conclusiveacross various machine learning models. Our preferred model outperforms theconventional approach with an RMSE value of 1.61 compared to an RMSE value of1.75 from the conventional approach.

Boosting Stock Price Prediction with Anticipated Macro Policy Changes