2025

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Browsing 2025 by Author "Hassaan Ahmad"

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    Leveraging Simplified Machine Learning for KSE-100 Index Prediction
    (UMT, Lahore, 2025) Hassaan Ahmad
    The Karachi Stock Exchange 100 (KSE-100) Index, a benchmark of Pakistan's stock market, reflects the performance of the top 100 companies and serves as a vital indicator of the country's economic health. However, the stochastic and volatile nature of stock markets, coupled with the complexity of analyzing numerous macroeconomic and sectoral variables, makes forecasting the KSE-100 Index a daunting task. Traditional time series models like ARIMA and ARMA, though foundational, struggle to capture the nonlinear dynamics and high volatility inherent in financial data. This research explores the transformative potential of machine learning (ML) techniques in addressing these challenges. By employing Random Forest, Gradient Boosting, and Support Vector Regression, the study evaluates their efficacy in predicting the KSE-100 Index. Using historical data spanning December 2019 to June 2024, the research integrates key macroeconomic variables such as GDP growth, inflation, and trading volume, enhancing the predictive power of these models. Among the models tested, Gradient Boosting emerged as the most effective, achieving an R² of 0.91, highlighting its ability to capture complex patterns and dependencies. Key contributions of this thesis include simplifying predictive frameworks, enabling accessibility for non-experts, and proposing a scalable approach that balances high accuracy with usability. The integration of feature engineering techniques, such as moving averages and volatility metrics, further underscores the robustness of the proposed methodology. Additionally, the research highlights critical gaps, such as the lack of real-time data integration and the need for user-friendly forecasting tools, paving the way for future advancements. This thesis bridges the gap between advanced financial forecasting techniques and practical applications, offering a democratized approach to stock market prediction. By empowering individual investors and small-scale firms, this study contributes to a more inclusive financial ecosystem and encourages informed decision-making in a time of economic uncertainty.