The final chapter summarizes the research results, highlighting that the ensemble method outperforms both individual models and the S&P 500 index benchmark in risk-adjusted returns. It reflects on the study’s contributions to understanding the relationship between financial indicators and stock market performance, highlighting the practical applications of machine learning in investment strategy formulation. The chapter also discusses the weaknesses of the present research and mentions the directions for future work, which can be used for the improvement and the subsequent research in finance and machine learning.