The impact of AI on stock market predictions

The stock market is a complex and dynamic system influenced by a wide range of economic, political, andpsychological factors. Predicting its behavior has long been a challenge for investors, analysts, and researchers.Traditional methods of stock market forecasting, such as technical and fundamental analysis, often fall short incapturing the non-linear and volatile nature of financial markets. With the advent of Artificial Intelligence (AI),particularly Machine Learning (ML) and Deep Learning (DL) techniques, a new frontier in stock marketprediction has emerged. This thesis explores the impact of AI on stock market prediction, focusing on its abilityto enhance accuracy, processing speed, and decision-making in financial investments.The study investigates the application of various AI models, including Random Forest, Support VectorMachines (SVM), and Long Short-Term Memory (LSTM) networks, in forecasting stock prices. It alsoincorporates Natural Language Processing (NLP) techniques to analyze market sentiment derived fromfinancial news and social media platforms such as Twitter and Reddit. By integrating both quantitative andqualitative data, the study aims to demonstrate how AI can provide a more comprehensive and real-timeassessment of market trends.