Abstract
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.
