Reference: | 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. |