DESIGN AND IMPLEMENTATION OF A PREDICTIVE MODEL FOR STOCK MARKET TRENDS
Abstract
This research project focuses on the development of a predictive model for stock market trends using Recurrent Neural Networks (RNN) with a Long Short-Term Memory (LSTM) architecture. The study addresses the challenges of market volatility and the limitations of traditional technical analysis by leveraging the strengths of LSTM networks in capturing temporal dependencies and long-term patterns in financial time series data. The model is trained using diverse datasets, including historical stock prices, trading volumes, and market sentiment extracted through natural language processing (NLP) of financial news and social media data. Additional features such as macroeconomic indicators and company financial reports are incorporated to enhance the robustness of the input data through a comprehensive feature engineering process.
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