Citation
Ishak, Iskandar and Ahmad Zukarnain, Zuriati and Sidi, Fatimah and Ibrahim, Hamidah and Palaniappan, Visalakshi
(2024)
High-frequency trading data forecasting model using quantum-based approach.
In: International Symposium on Applied Engineering and Sciences (SAES2024), 14-15 Nov. 2024, Fukuoka, Japan. .
Abstract
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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Official URL or Download Paper: https://conferenceservice.jp/www/saes2024/doc/SAES...
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Additional Metadata
| Item Type: | Conference or Workshop Item (Oral/Paper) |
|---|---|
| Divisions: | Faculty of Computer Science and Information Technology |
| Publisher: | Kyushu Institute of Technology |
| Keywords: | Quantum computing; Support vector machine; High frequency trading |
| Depositing User: | Mr. Mohamad Syahrul Nizam Md Ishak |
| Date Deposited: | 30 Oct 2025 08:48 |
| Last Modified: | 30 Oct 2025 08:48 |
| URI: | http://psasir.upm.edu.my/id/eprint/121377 |
| Statistic Details: | View Download Statistic |
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