Design, Development, and Performance Evaluation of a Machine Learning-Based Predictive Framework for Stock Market Trend Analysis

The design, development, and evaluation of the performance of a ML-based predictive model for stock market trend analysis were presented in the study. Historical stock market data was used to implement and evaluate multiple ML models such as DT, RF, SVM, XGBoost and LSTM models. The experimental results showed that the proposed framework resulted in better predictive performance with an accuracy of 97.83%, and improved precision, recall, F1-score, and ROC-AUC scores when compared to the traditional methods, with lower prediction error. The framework was also found to be computationally efficient, which is crucial for financial forecasting applications. As a whole, the proposed framework offers a dependable, scalable, and intelligent approach to stock market trend prediction and assists in the data-driven decision-making process for investments. Future research plans involve integrating deep learning models with real-time streaming data along with sentiment analysis capabilities to further boost forecasting accuracy and adaptability.

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