Comparative Study of Different Machine Learning Methodology Used for Stock Price Prediction

Stock price prediction has been a topic of interest for investors and researchers for a long time because of the monetary returns that can be achieved using correct forecasts. Over the last few years, machine learning (ML) approaches have become increasingly popular for modelling non-linear and complex financial time series data. This article offers a comparative study of three highly acclaimed machine learning approaches Long-Short-Term-Memory (LSTM) networks, Support Vector Machines (SVM), and Random Forests (RF) for forecasting stock prices. LSTM, an RNN variant, is designed to recognize long-term dependence in sequential information and thus becomes particularly appropriate to model time series trends. SVM, a versatile supervised learning approach, is characterized by its capacity to handle high-dimensional spaces with ease and express non-linear dependencies via kernel maps. Random Forest, an approach to ensemble-based learning, predicts multiple decision tree outputs to circumvent overfitting and provide better generalizability. Historical stock prices are employed to train and test each model in this research. Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²) Score are used to measure prediction accuracy. The findings show that all three models excel in various contexts, but LSTM performs better in identifying temporal patterns, SVM is good with smaller datasets and evident margins, and Random Forest provides robustness and interpretability. This comparative study sheds light on the strengths and weaknesses of each method, helping practitioners and researchers choose the most suitable model for stock market forecasting applications.