Financial Market Prediction using Deep Learning Models: A Comparative Analysis of Trading Strategies
Abstract
This dissertation investigates the effectiveness of three deep learning architectures- LSTM, CNN, and Transformer models —in predicting financial market movements using Bitcoin historical data. The study compares model performance using MAE, RMSE, and R² metrics, and develops a CNN-based trading strategy that achieved a 39.6% return on a backtested portfolio. Results demonstrate that the CNN model outperforms both LSTM and Transformer models, achieving the lowest error rates (MAE: 0.0156, R²: 0.9909).
Fields of Study & Subjects
Stock Market Forecasting Methods
Financial Markets and Investment Strategies
Blockchain Technology Applications and Security
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 5 March 2026
- Language
- EN
- Document type
- Thesis