Abstract
Emerging trends in deep learning, reinforcement learning, and natural language processing (NLP) hold immense promise for enhancing predictive capabilities in financial markets. Deep learning architectures such as transformers and graph neural networks are poised to revolutionize how complex market relationships and temporal dependencies are captured and understood. By leveraging these advancements, financial forecasting could achieve superior accuracy and resilience against market volatility. The evolution of AI-driven autonomous agents capable of executing trades based on predictive models represents a transformative shift in trading strategies. These agents, empowered by machine learning algorithms and real-time data analytics, can react swiftly to market signals, optimizing portfolio performance dynamically. However, the adoption of reliable AI-driven trading systems necessitates addressing challenges related to model interpretability, risk management, and regulatory compliance.
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