What type of study is this?

September 10, 2025

Algorithmic trading using deep learning: Opportunities, challenges and future directions

Puntos clave

Deep learning improves the accuracy of financial market predictions, enhancing trading strategies.
Advanced architectures like reinforcement learning and neural networks contribute to dynamic decision-making.
Challenges such as data quality and model interpretability must be addressed for robust trading systems.
Future directions focus on enhancing model robustness to improve overall trading performance.

Resumen

Algorithmic trading leveraging deep learning presents significant opportunities to enhance the accuracy and efficiency of financial market predictions by capturing complex patterns in vast datasets. This paper investigates the integration of advanced deep learning architectures, such as deep reinforcement learning and recurrent neural networks, to develop adaptive trading strategies capable of dynamic decision-making under market uncertainties. It also explores the challenges related to data quality, model interpretability, and overfitting, proposing future directions to address these issues and improve robustness. Ultimately, this study aims to contribute to the evolution of intelligent, data-driven algorithmic trading systems with superior performance and risk management capabilities.

Me gusta

Guardar

Cite This Study

Amin et al. (Wed,) studied this question.

synapsesocial.com/papers/68c1a26954b1d3bfb60ddb05 https://doi.org/https://doi.org/10.30574/ijsra.2025.16.1.2252

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

Me gusta

Guardar