Use of artificial intelligence for the prediction of stock market movements

Abstract

This article examines the use of artificial intelligence (AI) to predict stock market movements. It presents a theoretical model called the AI-Enhanced Secondary Market Prediction Framework (AISEMPF), which integrates various AI techniques and algorithms to analyze historical market data and identify variables and trends that can provide insights into future market behaviors. AISEMPF relies on the collection and preprocessing of large sets of historical data from reliable sources. These data are then used to train machine learning algorithms such as neural networks, decision trees, and support vector machines. These algorithms learn from historical data to detect patterns and relationships in past market movements. This theoretical model goes beyond historical data by incorporating external variables such as economic indicators, news sentiment analysis, and social media activity. By taking these factors into account, AISEMPF aims to improve its predictive accuracy by capturing additional information that may influence market movements. AISEMPF performs predictive analysis using trained machine learning algorithms and integrated external variables. It generates forecasts to provide insights into potential market trends or patterns. The performance of AISEMPF is evaluated by comparing the forecasts with actual market movements. The article highlights the importance of measuring accuracy, precision, and other relevant parameters to assess the model's performance.