AI for Financial Market Prediction: Machine Learning Models for Stock Price Forecasting

Stock market prediction is a complex and challenging task due to financial markets' volatility, uncertainty, and sensitivity. Conventional forecasting methods, namely fundamental and technical analysis, do not fundamentally consider the complex patterns, nonlinearity, and interdependent dynamics that drive stock price movements. In this work, we introduce a novel hybrid AIdriven method that aims to enhance predictive accuracy and reliability via an ensemble prediction model where the contributions of distinct ML models, Random Forest, XGBoost, Support Vector Machine (SVM), and Light GBM, combine. It highlights the necessity for good data cleaning, feature selection, and optimization of relevant financial indicators with minimum noise and discrepancies in data. Based on the results, through repetitive performance testing, Random Forest outperformed the other models with an $\mathrm{R}^{2}$ value of 0.9789, Mean Squared Error (MSE) value of 83722.04, and Mean Absolute Error (MAE) value of 25000.50 proved its reliability over the stock market dataset reflecting its ability to predict stable outcomes. The dualism significantly improves predictive stability and generalization, countering blocking problems such as overfitting and sensitivity to market turbulence. The study suggests potential areas for future research, such as adding deep learning approaches to improve prediction accuracy and employing real-time processing of financial data to enhance model adaptability. This research adds to the portfolio of AI-driven financial analytics, highlighting the capacity of integrated machine learning algorithms in predicting stock market trends and aiding investors in more accurately assessing their investment trajectories.