Comparison of Different Machine Learning Algorithms for Stock Price Prediction

Stock price prediction is a challenging and dynamic task due to the high volatility and complexity of financial markets. In this study, we present a comparative analysis of several machine learning algorithms applied to the prediction of stock prices. The models explored include Gradient Boosting Regressor (GBR), Gated Recurrent Unit (GRU), Linear Regression (LR), Long Short-Term Memory (LSTM), Random Forest (RF), and Support Vector Regression (SVR). Using a dataset comprising historical stock prices from multiple companies, we evaluate the performance of each model based on accuracy and predictive stability. Daily predictions were generated for each model, and the results were assessed using metrics such as average deviation from real price. The analysis indicates that while deep learning models like GRU and LSTM exhibit a strong capacity to capture temporal patterns, ensemble methods such as GBR and RF also provide competitive accuracy for short-term forecasts. This research highlights the strengths and limitations of various approaches to stock price forecasting and offers insights into their potential applications for investment decision-making. Future work may involve extending this analysis to broader financial datasets and investigating the integration of additional market factors.