Stock Data Analysis by Machine Learning Techniques to Improve Predictive Model-ing and Decision-Making in Financial Markets
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Abstract
Stock price predictions remain a significant area of interest, particularly among experts in computer science and artificial intelligence, due to the dynamic nature of financial markets. Numerous studies have employed machine learning techniques to forecast stock market trends—an essential task for investors aiming to maximize returns. This study investigates the predictive capabilities of several machine learning models, including Support Vector Machine (SVM), Gradient Descent, Random Forest, and Adaptive Boosting. The models are applied to predict the behavior of the Bombay Stock Exchange (BSE), using key input features such as crude oil prices, gold and silver values, historical market performance, and foreign exchange rates. The predictive performance of each model is evaluated and compared. Among the factors considered, gold prices exhibit the strongest positive correlation with market performance. Notably, the Adaptive Boosting method outperforms the other models, demonstrating its potential for accurate stock market forecasting.
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Journal of Studies in Science and Engineering is licensed under a Creative Commons Attribution License 4.0 (CC BY-4.0).
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