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題名 量子糾纏隨機遊走在雙資產價格預測中的應用
An Application of Entangled Quantum Walks to Dual-Asset Price Prediction
作者 楊叮噹
Yang, Ding-Dang
貢獻者 王國樑<br>張晏瑞
楊叮噹
Yang, Ding-Dang
關鍵詞 雙資產量子糾纏行走
雙資產市場價格波動
資產依存
量子金融
Dual-Asset Entangled Quantum Walk
Price Dynamics in the Dual-asset Markets
Asset Dependency
Quantum Finance
日期 2025
上傳時間 1-Jul-2025 15:34:54 (UTC+8)
摘要 本研究提出一套創新的雙資產量子行走模型，用以模擬投資者行為對雙資產市場價格波動的影響。該模型在捕捉金融資產間關係與複雜交互作用方面表現優異，展現出卓越的預測準確性，並能廣泛適用於不同時間區間與標的資產型態，顯示出高度的一般化能力。
This research introduces an innovative Dual-Assets Entangled Quantum Walk (DEQW) model to simulate the impact of investors’ behavior on price dynamics in the dual-asset markets. The model excels in capturing dependencies and complex interactions between financial assets, demonstrating high predictive accuracy and strong generalization across various timeframes and asset types.
參考文獻 1. Bachelier, L. (1900). Théorie de la spéculation. Annales scientifiques de l'École normale supérieure, 2. Childs, A. M. (2009). Universal computation by quantum walk. Physical review letters, 102(18), 180501. 3. Childs, A. M., Cleve, R., Deotto, E., Farhi, E., Gutmann, S., & Spielman, D. A. (2003). Exponential algorithmic speedup by a quantum walk. Proceedings of the thirty-fifth annual ACM symposium on Theory of computing. Proceedings of the thirty-fifth annual ACM symposium on Theory of computing , 59-68. 4. Cont, R. (2001). Empirical properties of asset returns: stylized facts and statistical issues. Quantitative finance, 1(2), 223. 5. Dixon, M. F., Halperin, I., & Bilokon, P. (2020). Machine learning in finance (Vol. 1170). Springer. 6. Egger, D. J., Gambella, C., Marecek, J., McFaddin, S., Mevissen, M., Raymond, R., Simonetto, A., Woerner, S., & Yndurain, E. (2020). Quantum computing for finance: State-of-the-art and future prospects. IEEE Transactions on Quantum Engineering, 1, 1-24. 7. Fama, E. F. (1970). Efficient capital markets. Journal of finance, 25(2), 383-417. 8. Fama, E. F. (2014). Two pillars of asset pricing. American Economic Review, 104(6), 1467-1485. 9. Gerlein, E. A., McGinnity, M., Belatreche, A., & Coleman, S. (2016). Evaluating machine learning classification for financial trading: An empirical approach. Expert Systems with Applications, 54, 193-207. 10. Glasserman, P. (2004). Monte Carlo methods in financial engineering (Vol. 53). Springer. 11. Kempe, J. (2003). Quantum random walks: an introductory overview. Contemporary Physics, 44(4), 307-327. 12. Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The annals of mathematical statistics, 22(1), 79-86. 13. Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 17(1), 59-82. 14. Malkiel, B. G. (2019). A random walk down Wall Street: the time-tested strategy for successful investing. WW Norton & Company. 15. Mantegna, R. N., & Stanley, H. E. (1999). Introduction to econophysics: correlations and complexity in finance. Cambridge university press. 16. Orús, R., Mugel, S., & Lizaso, E. (2019). Quantum computing for finance: Overview and prospects. Reviews in Physics, 4, 100028. 17. Ostaszewski, M., Grant, E., & Benedetti, M. (2021). Structure optimization for parameterized quantum circuits. Quantum, 5, 391. 18. Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied soft computing, 93, 106384. 19. Rebentrost, P., Gupt, B., & Bromley, T. R. (2018). Quantum computational finance: Monte Carlo pricing of financial derivatives. Physical Review A, 98(2), 022321. 20. Rebentrost, P., & Lloyd, S. (2024). Quantum computational finance: quantum algorithm for portfolio optimization. KI-Künstliche Intelligenz, 1-12. 21. Sirignano, J., & Cont, R. (2021). Universal features of price formation in financial markets: perspectives from deep learning. In Machine learning and AI in finance (pp. 5-15). Routledge. 22. Worthington, A., & Higgs, H. (2004). Random walks and market efficiency in European equity markets. The Global Journal of Finance and Economics, 1(1), 59-78.
描述 碩士
國立政治大學
經濟學系
112258018
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112258018
資料類型 thesis
dc.contributor.advisor 王國樑<br>張晏瑞zh_TW
dc.contributor.author (Authors) 楊叮噹zh_TW
dc.contributor.author (Authors) Yang, Ding-Dangen_US
dc.creator (作者) 楊叮噹zh_TW
dc.creator (作者) Yang, Ding-Dangen_US
dc.date (日期) 2025en_US
dc.date.accessioned 1-Jul-2025 15:34:54 (UTC+8)-
dc.date.available 1-Jul-2025 15:34:54 (UTC+8)-
dc.date.issued (上傳時間) 1-Jul-2025 15:34:54 (UTC+8)-
dc.identifier (Other Identifiers) G0112258018en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/157861-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 經濟學系zh_TW
dc.description (描述) 112258018zh_TW
dc.description.abstract (摘要) 本研究提出一套創新的雙資產量子行走模型，用以模擬投資者行為對雙資產市場價格波動的影響。該模型在捕捉金融資產間關係與複雜交互作用方面表現優異，展現出卓越的預測準確性，並能廣泛適用於不同時間區間與標的資產型態，顯示出高度的一般化能力。zh_TW
dc.description.abstract (摘要) This research introduces an innovative Dual-Assets Entangled Quantum Walk (DEQW) model to simulate the impact of investors’ behavior on price dynamics in the dual-asset markets. The model excels in capturing dependencies and complex interactions between financial assets, demonstrating high predictive accuracy and strong generalization across various timeframes and asset types.en_US
dc.description.tableofcontents 摘要 1 Abstract 2 Contents 3 List of Figures 5 List of Tables 6 I. Introduction 7 II. Methodology 9 2.1 Single-Asset Quantum Walk 9 2.2 Dual-Assets Entangled Quantum Walk 10 2.3 Time-Driven Prediction 12 2.4 Measurement-Based Probability Extraction 12 2.5 Expected Return Estimation 13 III. Experimental Configurations and Test Design 15 3.1 Measurement-Based Probability Extraction 15 3.2 Optimization Algorithms and Temporal Shift Parameters 16 3.3 Multi-Control Gate Configuration and Entanglement Desig 17 3.4 Multi-Scale Forecasting Framework and Dataset Composition 18 3.5 Taiwan Market and Multi-Period Forecasting 18 IV. Results 19 4.1 Baseline Configuration and Controlled Variations 19 4.2 Impact of Initialization and Gate Designs 23 4.3 Impact of Optimizer Selection and Temporal Configuration 25 4.4 Impact of Entanglement Scheduling and Directionality 26 4.5 Temporal Generalization and Forecasting Stability 27 4.6 Robustness and Generalization Across Financial Domains 27 4.7 Taiwan-Listed Assets and Multi-Period 31 V. Conclusion 34 VI. Reference 35zh_TW
dc.format.extent 837870 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112258018en_US
dc.subject (關鍵詞) 雙資產量子糾纏行走zh_TW
dc.subject (關鍵詞) 雙資產市場價格波動zh_TW
dc.subject (關鍵詞) 資產依存zh_TW
dc.subject (關鍵詞) 量子金融zh_TW
dc.subject (關鍵詞) Dual-Asset Entangled Quantum Walken_US
dc.subject (關鍵詞) Price Dynamics in the Dual-asset Marketsen_US
dc.subject (關鍵詞) Asset Dependencyen_US
dc.subject (關鍵詞) Quantum Financeen_US
dc.title (題名) 量子糾纏隨機遊走在雙資產價格預測中的應用zh_TW
dc.title (題名) An Application of Entangled Quantum Walks to Dual-Asset Price Predictionen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 1. Bachelier, L. (1900). Théorie de la spéculation. Annales scientifiques de l'École normale supérieure, 2. Childs, A. M. (2009). Universal computation by quantum walk. Physical review letters, 102(18), 180501. 3. Childs, A. M., Cleve, R., Deotto, E., Farhi, E., Gutmann, S., & Spielman, D. A. (2003). Exponential algorithmic speedup by a quantum walk. Proceedings of the thirty-fifth annual ACM symposium on Theory of computing. Proceedings of the thirty-fifth annual ACM symposium on Theory of computing , 59-68. 4. Cont, R. (2001). Empirical properties of asset returns: stylized facts and statistical issues. Quantitative finance, 1(2), 223. 5. Dixon, M. F., Halperin, I., & Bilokon, P. (2020). Machine learning in finance (Vol. 1170). Springer. 6. Egger, D. J., Gambella, C., Marecek, J., McFaddin, S., Mevissen, M., Raymond, R., Simonetto, A., Woerner, S., & Yndurain, E. (2020). Quantum computing for finance: State-of-the-art and future prospects. IEEE Transactions on Quantum Engineering, 1, 1-24. 7. Fama, E. F. (1970). Efficient capital markets. Journal of finance, 25(2), 383-417. 8. Fama, E. F. (2014). Two pillars of asset pricing. American Economic Review, 104(6), 1467-1485. 9. Gerlein, E. A., McGinnity, M., Belatreche, A., & Coleman, S. (2016). Evaluating machine learning classification for financial trading: An empirical approach. Expert Systems with Applications, 54, 193-207. 10. Glasserman, P. (2004). Monte Carlo methods in financial engineering (Vol. 53). Springer. 11. Kempe, J. (2003). Quantum random walks: an introductory overview. Contemporary Physics, 44(4), 307-327. 12. Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The annals of mathematical statistics, 22(1), 79-86. 13. Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 17(1), 59-82. 14. Malkiel, B. G. (2019). A random walk down Wall Street: the time-tested strategy for successful investing. WW Norton & Company. 15. Mantegna, R. N., & Stanley, H. E. (1999). Introduction to econophysics: correlations and complexity in finance. Cambridge university press. 16. Orús, R., Mugel, S., & Lizaso, E. (2019). Quantum computing for finance: Overview and prospects. Reviews in Physics, 4, 100028. 17. Ostaszewski, M., Grant, E., & Benedetti, M. (2021). Structure optimization for parameterized quantum circuits. Quantum, 5, 391. 18. Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied soft computing, 93, 106384. 19. Rebentrost, P., Gupt, B., & Bromley, T. R. (2018). Quantum computational finance: Monte Carlo pricing of financial derivatives. Physical Review A, 98(2), 022321. 20. Rebentrost, P., & Lloyd, S. (2024). Quantum computational finance: quantum algorithm for portfolio optimization. KI-Künstliche Intelligenz, 1-12. 21. Sirignano, J., & Cont, R. (2021). Universal features of price formation in financial markets: perspectives from deep learning. In Machine learning and AI in finance (pp. 5-15). Routledge. 22. Worthington, A., & Higgs, H. (2004). Random walks and market efficiency in European equity markets. The Global Journal of Finance and Economics, 1(1), 59-78.zh_TW