Quantum-Inspired AI for Optimized High-Frequency Trading
This article explores the transformative role of quantum-inspired AI in optimizing financial practices, particularly within high-frequency trading (HFT) in the financial sector. As HFT operates in an environment of rapid transactions and significant market volatility, the need for advanced optimization techniques becomes paramount. Quantum-inspired algorithms leverage principles from quantum mechanics, such as superposition and tunneling, to enhance various aspects of trading strategies. These algorithms enable rapid optimization of asset allocation, real-time trade execution, and proactive fraud detection, effectively addressing the challenges posed by traditional financial models. By facilitating simultaneous evaluation of multiple strategies and enabling real-time analysis of complex trading patterns, quantum-inspired AI significantly improves decision-making speed and accuracy. The financial implications of this advancement are profound, leading to higher profitability, improved market integrity, and enhanced trust among market participants. Ultimately, integrating quantum-inspired AI in finance represents a crucial step towards harnessing cutting-edge technology to reshape trading dynamics, paving the way for innovative strategies that can adapt to the evolving landscape of financial markets. This study underscores the potential of quantum-inspired AI to redefine operational efficiency in finance, ensuring competitiveness in an increasingly complex trading environment.
Authors
- Manoj Kumar Vandanapu
- Asmath Shaik
- Satish Kumar Nagamalla
- Radharani Balbhadruni
Publication Details
- Journal
- International Journal of Finance
- Published
- 2024-10-19
- DOI
- https://doi.org/10.47941/ijf.2301
- Primary Topic
- Stochastic processes and financial applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00