MACHINE LEARNING FOR STOCK MARKET SECURITY MEASUREMENT: A COMPARATIVE ANALYSIS OF SUPERVISED, UNSUPERVISED, AND DEEP LEARNING MODELS

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This study presents a comprehensive analysis of machine learning techniques for measuring and predicting security in stock markets, comparing the performance of supervised, unsupervised, and deep learning models. Using a diverse dataset from Kaggle that includes historical stock prices, financial news sentiment, company fundamentals, and macroeconomic indicators, we applied feature engineering and rigorous preprocessing methods to optimize model accuracy. The study evaluated Random Forest, Support Vector Machines (SVM), K-Means clustering, and Long Short-Term Memory (LSTM) networks across key performance metrics. Results indicate that Random Forest outperformed other models in classification tasks with an accuracy of 92%, making it highly effective for real-time security assessment. SVM also demonstrated strong classification capabilities, particularly in high-dimensional spaces, with an accuracy of 88%. K-Means and DBSCAN clustering algorithms excelled in anomaly detection, identifying unusual patterns that could signal market irregularities. LSTM models, designed for time-series forecasting, achieved a root mean square error (RMSE) of 1.78, proving their utility in predicting future stock trends but requiring more computational resources.Our findings suggest that a hybrid approach, combining the strengths of supervised and deep learning models, can provide a robust solution for stock market security measurement. By leveraging explainable AI techniques such as SHAP and LIME, we also improved model interpretability, making these predictions more actionable for stakeholders. This research highlights the potential of machine learning in financial security monitoring and supports the growing integration of AI in the finance industry.