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Physics World

Quantum physics meets classical finance

Paul M. Darbyshire

Published May 1, 2005• 9 citations
Publisher

Abstract

Every DAY, trillions of dollars are traded on the world's financial markets. The bulk of these transactions are carried out by traders working in banks, who stake investors' money on financial "instruments" such as the FTSE-100 share index or the exchange rate between two currencies. However, as investors have demanded greater potential returns on their investments, this basic trading activity has become increasingly more sophisticated.

DOI
10.1088/2058-7058/18/5/36

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Every DAY, trillions of dollars are traded on the world's financial markets. The bulk of these transactions are carried out by traders working in banks, who stake investors' money on financial "instruments" such as the FTSE-100 share index or the exchange rate between two currencies. However, as investors have demanded greater potential returns on their investments, this basic trading activity has become increasingly more sophisticated.
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A financial market is a trading system for financial assets such as derivatives, stocks, currencies, and bonds. Financial markets, which can be virtual or physical, are essential to the world economy. Financial forecasting has become an enormous difficulty due to the markets’ quick internationalization and the volume of data gathered from various sources growing at an accelerating rate. The study explores the creation of a model for stock market forecasting that leverages the power of big data analytics and quantum computing (QC). By handling multifaceted datasets and achieving high-accuracy forecasting, big data and QC improve market trends and insights. This is achieved by leveraging QC speed-driven problem-solving capabilities. In this study, we proposed a novel Hippopotamus Optimized Quantum Refined Support Vector Machine (HO-QRSVM) to predict the financial market. The financial dataset came from several places, such as economic databases and stock exchanges. The preprocessed data from the acquired data were cleaned and normalized. To remove dimensionality and extract features from preprocessed data, principle component analysis (PCA) is used. The capacity of QC to handle entanglement and superposition enables the simultaneous investigation of several potential market situations, leading to faster convergence and more precise forecasts. The result demonstrated the proposed method has significant improvements in prediction accuracy compared to other traditional algorithms. The performance evaluation techniques include the F1-score (96.3%), recall (98.2%), accuracy (98.5%), and precision (98.3%). The research, big data analytics, and QC combined can greatly improve financial market forecasts, giving decision-makers and strategic investors an advantage over their competition.
This chapter demonstrates how financial markets exhibit quantum-like behavior that challenges traditional deterministic approaches. Through wave-particle duality, it shows how financial assets appear as smooth trends to long-term investors but as discrete jumps to high-frequency traders, with both views being valid. Key concepts include the uncertainty principle’s trade-offs in financial reporting (accuracy versus timeliness), the observer effect where measurement changes outcomes, and financial indeterminacy where values exist as probability ranges rather than fixed points. The chapter examines practical quantum applications, including interpretive superposition in financial statements, cognitive dissonance in performance evaluation, and valuation uncertainty. Major case studies of Enron (2001) and Long-Term Capital Management (LTCM; 1998) illustrate how quantum-like superpositions can collapse catastrophically when scrutinized. The chapter advocates embracing uncertainty as finance’s fundamental characteristic rather than a flaw that should be eliminated, establishing frameworks for quantum-inspired decision-making approaches.