Artificial Intelligence-Based Stock Market Prediction: A Comparative Analysis of Machine Learning and Deep Learning Models

Abstract

The stock market prediction is a crucial research field that could help investors make informed decisions, manage risks, and plan financial strategies. As financial markets grow more intricate and unpredictable, the usage of Artificial Intelligence (AI) methods for uncovering concealed patterns and connections amid massive amounts of monetary information has become more widespread.The complex and fluctuating nature of monetary markets has driven the usage of Artificial Intelligence (AI) strategies that can uncover hidden patterns and relationships from enormous amounts of monetary information. This research paper compares the different models of machine learning and deep learning applied to the stock market prediction. The research method used is literature-based research and the studies examined are those that have been published in the open access from 2020-2026. The models compared are machine learning models like Support Vector Machines (SVM), Random Forest, XGBoost, and deep learning architectures like Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Transformer-based networks. These models are analysed on the basis of their accuracy in prediction, computation requirement, interpretability, data dependency and forecasting ability. The results show that, generally speaking, deep learning models outperform conventional statistical models in predicting financial time-series data because they can learn from the temporal relationships and complex nonlinear relationships in financial time-series data. Transformer and LSTM models are the models with the best forecasting accuracy from the models you are looking at. Despite of its low computation cost, short training time, and high interpretability, however, machine learning techniques still have their significance. The study also identifies some important challenges such as data quality, market volatility, overfitting, and transparency of the models. Finally, research avenues including hybrid AI frameworks, explainable AI, sentiment-driven forecasting and cutting-edge attention-based architectures are explored. The results help enhance the comprehension of stock market prediction using AI and offer direction for picking proper forecasting models for financial applications.