Creating a Framework for Predicting Stock Prices Using Intelligent Media and Technical Analysis.

Abstract

This study centers on the creation of a comprehensive framework that leverages intelligent media and technical analysis for stock price prediction. Given the increasing complexity and volatility of financial markets, accurately forecasting stock prices has become a formidable task. To overcome this challenge, this research aims to integrate intelligent media analysis techniques with traditional technical analysis methods. The framework utilizes intelligent media analysis to collect and analyze pertinent news, social media sentiment, and market trends. By incorporating sentiment analysis and natural language processing, valuable insights are extracted from textual data, leading to a comprehensive understanding of market sentiment. Furthermore, traditional technical analysis techniques like chart pattern recognition, trend analysis, and statistical indicators are integrated to identify patterns and trends in historical stock price data. These technical analysis components form the basis for predicting future price movements.The suggested framework leverages machine learning algorithms to examine past data and detect patterns,and establish correlations that contribute to accurate stock price predictions. Through the training and optimization of models using a large dataset, the framework aims to enhance prediction accuracy and reliability. The outcomes of this research will advance the development of a practical tool designed to aid investors, financial analysts, and traders in making well-informed decisions regarding stock investments. The integration of intelligent media analysis and technical analysis within a unified framework has the potential to augment stock price prediction accuracy and provide valuable insights into market dynamics. Keywords : stock price prediction,SVR,RFR,KNN,LSTM,GRU, Technical Analysis,Sentiment Analysis, Machine Learning, Pattern Recognition, Market Sentiment, Financial Markets