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Quantum-Inspired AI for Optimized High-Frequency Trading

Author

Listed:
  • Manoj Kumar Vandanapu
  • Asmath Shaik
  • Satish Kumar Nagamalla
  • Radharani Balbhadruni

Abstract

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.

Suggested Citation

  • Manoj Kumar Vandanapu & Asmath Shaik & Satish Kumar Nagamalla & Radharani Balbhadruni, 2024. "Quantum-Inspired AI for Optimized High-Frequency Trading," International Journal of Finance, CARI Journals Limited, vol. 9(7), pages 1-17.
  • Handle: RePEc:bhx:ojtijf:v:9:y:2024:i:7:p:1-17:id:2301
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    File URL: https://carijournals.org/journals/index.php/IJF/article/view/2301/2711
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