Quantum Finance: Intelligent Forecast and Trading Systems

Tushar Saini, Kashish Solanki, Sandeeo Raj +1 more

Karthikeya Gummadi

R. Deepa, Arasuraja Ganesan, Umidbek M. Abdalov +3 more

Magesh S, V. Sutha, Layth Hussein +3 more

Forecasting and managing risk incurring well are major tasks among financial markets that are very volatile, highly interdependent, and with huge streams of data. Unfortunately, these circumstances cannot be handled by conventional computational models due to their inability to work with large data sets and to identify the nonlinear patterns in finance. Quantum computers (QCs) have revolutionized how financial decisions are being made with the new quantum parallelism and excellent optimization algorithms. In this work, Quantum Powered Financial Intelligence (QPFI) System, as a combination of Quantum Machine Learning (QML), Quantum Optimization, and Quantum Monte Carlo Simulations is offered to optimize market forecast and risk estimation. Better pattern detection is realized to predict asset prices using QML models, for example, Variational Quantum Circuits and Quantum Support Vector Machines. Quantum Approximate Optimization Algorithms are used for portfolio allocation optimization of risk vs. return tradeoffs and quantum Monte Carlo simulations for speed-up in stress testing and credit risk assessment. Another way fraud detection is helped by QGANs is in detecting any concealed anomalies in transactional data. The suggested hybrid quantum-classical solution is scalable through cloud-based quantum infrastructure, which facilitates pragmatic implementation within financial institutions. Through the presentation of improved accuracy, computational power, and real-time decision-making, this study identifies the prospect of quantum computing to revolutionize risk management and forecasting models in financial institutions. Future research directions involve enhancing hardware stability, regulatory aspects, and practical quantum trials in banking and trading companies.

Eugene R. Serov, Sergey Anatolievich Vasiliev

Aim. IThe presented study aims to identify current trends and key directions for the introduction of quantum technologies into the management practices of Russian credit institutions to improve their performance in the medium term. Tasks. The authors comparatively analyze current trends in the application of quantum technologies in the banking business; forecast key trends in their development in the medium term in the context of the Russian banking sector. Methods. The authors use the methods of comparative and system analysis, scientific abstraction, and hypothesis. Results. Practical examples of the introduction of modern quantum technologies in the foreign and Russian banking business are provided. As a result, priority directions and areas of their application are identified with an emphasis on the domestic banking sector: credit scoring, assessment of the value and risk of investment, diversification of investment portfolios, financial forecasting, crisis prediction, fraud detection, protection of banking and customer databases from the quantum threat, and increasing the level of banking cybersecurity in general. Conclusions. Despite the novelty of quantum technologies in the banking business, the role they will play in banking in the near future is difficult to overestimate. Quantum computers can significantly improve the performance of banking IT systems by solving computational tasks that even super-powerful conventional computers are not able to tackle on their own. They significantly increase the efficiency of modeling complex systems and processes; accelerate the soultion of problems associated with resource optimization, logistics, finance, and trading; simplify work with big data using artificial intelligence (AI) and machine learning models in neural networks; help to combat cyber attacks and fraud in financial markets.

Isaiah Hull, Or Sattath, Eleni Diamanti +1 more