Stock Market Forecast through Neural Networks

The objective of this thesis project is to forecast stock markets using various types of neural network models, including Graph Convolutional Networks (GCNs), Long Short Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). The data used for this forecasting task consist of ETF tickers and stocks. By implementing these advanced neural network models, the project aims to improve the accuracy of stock market predictions.

The objective of this thesis project is to forecast stock markets using various types of neural network models, including Graph Convolutional Networks (GCNs), Long Short Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs). The data used for this forecasting task consist of ETF tickers and Stocks. By implementing these advanced neural network models, the project aims to improve the accuracy of Stock market predictions.