AI-Driven Algorithmic Trading with Real-Time Risk Management: Integrating Control Systems for Optimized Portfolio Management

EasyChair Preprint 14480

Abstract

This paper explores the innovative integration of artificial intelligence (AI) in algorithmic trading, with a focus on real-time risk management. In an era where financial markets are increasingly driven by rapid and complex data exchanges, AI offers unprecedented capabilities in analyzing vast amounts of market data, predicting trends, and making instantaneous trading decisions. The study delves into the mechanisms of AI-driven trading algorithms, highlighting how machine learning models, particularly those leveraging deep learning and natural language processing (NLP), enhance decision-making processes by providing real-time insights into market sentiment and risk factors. Furthermore, the paper examines the implementation of real-time risk management systems that dynamically adjust trading strategies in response to fluctuating market conditions, thereby mitigating potential losses and optimizing returns. By combining advanced AI techniques with robust risk management frameworks, this research demonstrates a significant advancement in the field of algorithmic trading, offering a pathway to more efficient and secure trading operations. The findings suggest that AI-driven algorithmic trading not only improves market performance but also contributes to a more resilient financial ecosystem.

Keyphrases: AI-Driven Algorithmic Trading, Natural Language Processing (NLP), Recurrent Neural Networks (RNNs)

@booklet{EasyChair:14480,
  author    = {Abi Cit},
  title     = {AI-Driven Algorithmic Trading with Real-Time Risk Management: Integrating Control Systems for Optimized Portfolio Management},
  howpublished = {EasyChair Preprint 14480},
  year      = {EasyChair, 2024}}