Abstract:
"This research focuses on developing a predictive model to forecast future stock prices of
companies within the ""Materials"" industry listed in the “Colombo Stock Exchange”, by
leveraging financial ratios. Utilizing a robust dataset comprising various financial
ratios—such as Price-to-Earnings Ratio, Debt-to-Equity Ratio, Return on Equity, and many
others—this study aims to identify the most significant predictors of stock price movements
within this sector. By employing advanced statistical techniques and machine learning
algorithms through tools such as Pycharm for coding and Power BI for data visualization and
analysis, we systematically analyze historical financial data to construct a model that can
accurately predict future stock prices.
The study begins with a comprehensive literature review to identify previously established
correlations between financial ratios and stock performance. Following data collection and
preprocessing, we apply multiple regression analysis & decision tree regression to evaluate
the predictive power of each financial ratio. The model's performance is assessed using a
split-sample test, with a focus on metrics such as R-squared, mean squared error (MSE), and
accuracy percentage to ensure reliability and validity.
Our findings reveal that certain financial ratios hold significant predictive capability for stock
prices in the Materials industry, offering insights into the financial health and operational
efficiency of firms within this sector. The predictive model developed in this research not
only enhances investment decision-making but also contributes to the academic literature by
providing a focused analysis on the Materials industry. Moreover, it offers a framework that
can be adapted and applied to other sectors for forecasting stock prices based on financial
health indicators. Through this study, we demonstrate the practical applications of financial
ratios in stock market analysis and the potential for predictive analytics in enhancing market
efficiency and investment strategies."
