Application of Artificial Intelligence in Stock Market Prediction

Abstract

Because of the inherent volatility and non-linearity of financial markets, precise forecasting is a constant struggle. Using historical data, this study examines how well intelligent algorithms in forecast trading market trends. It contrasts a number of machine learning methods, such as Random Forest, K-Nearest Neighbors (KNN), XGBoost, and Decision Tree, with a deep learning methodology based on Long Short-Term Memory (LSTM) networks. It is possible to assess these models’ capacity to identify intricate temporal patterns because they are trained directly on historical price data rather than using specially designed technical indicators. The findings provide insights into successful data-driven forecasting techniques by highlighting the advantages and disadvantages of each approach in various market scenarios. Supporting the creation of predictive tools for well-informed decision-making in trading environments is the goal of this research.