High-frequency trading data forecasting model using quantum-based approach
This research attempts to enhance SVM model optimization through Quantum computing. Quantum approach performs calculations based on qubits, where the probability of an object's state, instead of just 1s or 0s rather applied on superposition. This gives the potential to process exponentially...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
Kyushu Institute of Technology
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/121377/1/121377.pdf http://psasir.upm.edu.my/id/eprint/121377/ https://conferenceservice.jp/www/saes2024/doc/SAES2024_proceeding.pdf |
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| Summary: | This research attempts to enhance SVM model optimization through Quantum
computing. Quantum approach performs calculations based on qubits, where the probability of an
object's state, instead of just 1s or 0s rather applied on superposition. This gives the potential to
process exponentially more data compared to classical computers [2]. Datasets used are SP500
HFT dataset and Apple Inc. (AAPL) stocks from 2017 to 2018. |
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