Open Access

ARTICLE

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

Saket Sarin¹, Sunil K. Singh¹, Sudhakar Kumar¹, Shivam Goyal¹, Brij Bhooshan Gupta^2,3,4,8,*, Wadee Alhalabi⁵, Varsha Arya^6,7

1 Chandigarh College of Engineering and Technology, Chandigarh, 160019, India
2 Department of Computer Science and Information Engineering, Asia University, Taichung, 413, Taiwan
3 Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, 411057, India
4 Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, 248007, India
5 Department of Computer Science, Immersive Virtual Reality Research Group, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
6 Department of Business Administration, Asia University, Taichung, 413, Taiwan
7 Department of Electrical and Computer Engineering, Lebanese American University, Beirut, 1102 2801, Lebanon
8 UCRD, Chandigarh University, Chandigarh, 140413, India

* Corresponding Author: Brij Bhooshan Gupta. Email:

Computers, Materials & Continua 2024, 80(2), 3123-3138. https://doi.org/10.32604/cmc.2024.051599

Received 10 March 2024; Accepted 20 June 2024; Issue published 15 August 2024

Abstract

In the rapidly evolving landscape of today’s digital economy, Financial Technology (Fintech) emerges as a transformative force, propelled by the dynamic synergy between Artificial Intelligence (AI) and Algorithmic Trading. Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning (MARL) and Explainable AI (XAI) within Fintech, aiming to refine Algorithmic Trading strategies. Through meticulous examination, we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm, employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions. These AI-infused Fintech platforms harness collective intelligence to unearth trends, mitigate risks, and provide tailored financial guidance, fostering benefits for individuals and enterprises navigating the digital landscape. Our research holds the potential to revolutionize finance, opening doors to fresh avenues for investment and asset management in the digital age. Additionally, our statistical evaluation yields encouraging results, with metrics such as Accuracy = 0.85, Precision = 0.88, and F1 Score = 0.86, reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

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Keywords

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