Quantum Computing in Risk Assessment
Keywords:
Quantum Computing, FinanceAbstract
Quantum computing has emerged as a promising technology with the potential to revolutionize various industries, including finance. This paper explores the applications of quantum computing in finance, with a specific focus on portfolio optimization, risk assessment, and option pricing in financial markets. The paper begins by providing an overview of quantum computing principles and its advantages over classical computing for solving complex optimization problems. It then delves into the challenges and opportunities of integrating quantum computing into financial systems, highlighting the current state of research and development in this area.
The main focus of the paper is on portfolio optimization, which is a critical task in finance that involves selecting the optimal mix of assets to maximize returns while minimizing risk. Quantum computing offers the potential to significantly improve the efficiency and accuracy of portfolio optimization by leveraging quantum algorithms such as quantum annealing and quantum machine learning. The paper discusses the key concepts and algorithms involved in quantum portfolio optimization, highlighting their advantages and limitations compared to classical approaches.
In addition to portfolio optimization, the paper also examines the application of quantum computing in risk assessment and option pricing. Risk assessment involves analyzing and mitigating the risks associated with financial investments, while option pricing involves valuing financial derivatives such as options. Quantum computing can offer more accurate and efficient solutions for these tasks, potentially leading to better risk management and pricing strategies in financial markets.
Overall, this paper provides a comprehensive overview of the applications of quantum computing in finance, focusing on portfolio optimization, risk assessment, and option pricing. It discusses the current state of research and development in this field, highlighting the challenges and opportunities of integrating quantum computing into financial systems. The paper concludes with a discussion of future directions and potential advancements in quantum finance.
Downloads
References
Tatineni, Sumanth, and Anirudh Mustyala. "AI-Powered Automation in DevOps for Intelligent Release Management: Techniques for Reducing Deployment Failures and Improving Software Quality." Advances in Deep Learning Techniques 1.1 (2021): 74-110.
Biswas, A., and W. Talukdar. “Robustness of Structured Data Extraction from In-Plane Rotated Documents Using Multi-Modal Large Language Models (LLM)”. Journal of Artificial Intelligence Research, vol. 4, no. 1, Mar. 2024, pp. 176-95, https://thesciencebrigade.com/JAIR/article/view/219.
Bojja, Giridhar Reddy, Jun Liu, and Loknath Sai Ambati. "Health Information systems capabilities and Hospital performance-An SEM analysis." AMCIS. 2021.
Jeyaraman, Jawaharbabu, and Muthukrishnan Muthusubramanian. "Data Engineering Evolution: Embracing Cloud Computing, Machine Learning, and AI Technologies." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 1.1 (2023): 85-89.
Shahane, Vishal. "Towards Real-Time Automated Failure Detection and Self-Healing Mechanisms in Cloud Environments: A Comparative Analysis of Existing Systems." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 136-158.
Devan, Munivel, Ravish Tillu, and Lavanya Shanmugam. "Personalized Financial Recommendations: Real-Time AI-ML Analytics in Wealth Management." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)2.3 (2023): 547-559.
Abouelyazid, Mahmoud. "Natural Language Processing for Automated Customer Support in E-Commerce: Advanced Techniques for Intent Recognition and Response Generation." Journal of AI-Assisted Scientific Discovery 2.1 (2022): 195-232.
Prabhod, Kummaragunta Joel. "Utilizing Foundation Models and Reinforcement Learning for Intelligent Robotics: Enhancing Autonomous Task Performance in Dynamic Environments." Journal of Artificial Intelligence Research 2.2 (2022): 1-20.
Tatineni, Sumanth. "Applying DevOps Practices for Quality and Reliability Improvement in Cloud-Based Systems." Technix international journal for engineering research (TIJER)10.11 (2023): 374-380.
Althati, Chandrashekar, Manish Tomar, and Lavanya Shanmugam. "Enhancing Data Integration and Management: The Role of AI and Machine Learning in Modern Data Platforms." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 2.1 (2024): 220-232.
