Forecasting stock prices: Deep Learning and Full Order Book Dynamics

Key Points

• Stock prices can be effectively forecasted using deep learning techniques, enhancing prediction accuracy.
• Key evidence includes performance metrics indicating significant predictive ability against traditional methods.
• Analysis incorporates full order book dynamics to capture market microstructure effects on stock movements.
• These findings may enable more refined trading approaches, though further validation in diverse market conditions is necessary.

Abstract