Forecasting financial markets using advanced machine learning algorithms

Abstract

This article explores the application of advanced data analysis techniques in the financial sector using neural networks for price forecasting in financial markets. Neural networks, with their ability for self-learning and capturing complex dependencies, offer great potential for accurate financial trend predictions. The article describes the development and utilization of a mathematical model based on convolutional neural networks for forecasting the state of financial markets. The model is trained on historical data, uncovering hidden relationships among various factors and predicting future prices based on acquired knowledge. However, additional research and algorithm optimization are needed to further enhance the accuracy and reliability of the forecasts. The application of neural networks in financial market forecasting represents a crucial area of research that can significantly impact decision-making and the performance of financial operations. Improving the accuracy and reliability of such models can contribute to more effective risk management and better outcomes in the financial sector.

Keywords

• price forecasting
• financial markets
• data analysis
• neural networks
• self-learning