Effect of Data-Driven Perspective on Stock Market Dynamics Using Machine Learning: A Case Study of Nigeria Cement Industries

The Nigerian stock market, despite its vast potential, remains underutilized, hindering the country's economic growth. This underutilization stems from the inability of predictive models to forecast market trends with considerable accuracy, largely due to insufficient data and inadequate modelling techniques. The volatile nature of the market exacerbates poor investment decisions, resulting in significant losses for those who invest and discouraging potential investors. This research aims to develop a robust stock market prediction model using data-driven derivative which leverages on machine learning algorithms and statistical methods using the Nigeria's quoted cement companies on the Nigeria Stock Exchange (NSE). By uncovering meaningful patterns and trends, the model will provide valuable insights to inform investment decisions. The study employed a data-driven derivative approach on the utilized historical stock market data to train and test the model. The results demonstrate the model's effectiveness in predicting stock market trends, thereby promoting informed investment decisions. This research contributes to the development of a proven solution for stock market prediction, fostering economic growth, attracting investment, and improving Nigerians' overall well-being. By harnessing the potential of Nigeria's stock market, this study uncovers new opportunities for economic progress and prosperity.