Stock Market Forecasting Using Quantum Computing Simulation and News Based Sentiment Analysis
DOI:
https://doi.org/10.63282/3050-922X.ICAILLMBA-109Keywords:
Stock Market Forecasting, Quantum Computing Simulation, News-Based Sentiment Analysis, Variational Quantum Classifier, Financial Data AnalysisAbstract
Stock market forecasting is challenging due to high volatility, non-linearity, and the strong influence of external information such as financial news. Traditional forecasting models mainly rely on historical price data and often fail to respond effectively to sentiment-driven market fluctuations. To address this limitation, this paper proposes a hybrid stock market forecasting framework that integrates historical stock price indicators, news-based sentiment analysis, and quantum computing simulation. Financial news articles are processed to extract sentiment scores representing investor mood, which are combined with normalized price features to form a unified dataset. The combined features are analyzed using a Variational Quantum Classifier implemented through quantum simulation. Experimental results show that incorporating sentiment information improves prediction stability and accuracy compared to price-only models, demonstrating the effectiveness of quantum-inspired approaches for short-term market direction prediction.
References
[1] Securities and Exchange Board of India (SEBI), SEBI Annual Report 2023–24, SEBI, Mumbai, India, Aug. 2024.
[2] Securities and Exchange Board of India (SEBI), Reports & Statistics (Annual Reports Archive), SEBI, Mumbai, India.
[3] Reserve Bank of India (RBI), Financial Stability Report, December 2024, RBI, Mumbai, India.
[4] National Stock Exchange of India (NSE), All Reports / Historical Reports (Bhavcopy, Price–Volume Archives, Index Data), NSE, Mumbai, India.
[5] National Stock Exchange of India (NSE), Historical Reports – Capital Market (Daily/Monthly Archives), NSE, Mumbai, India.
[6] E. F. Fama, “Efficient Capital Markets: A Review of Theory and Empirical Work,” The Journal of Finance, vol. 25, no. 2, pp. 383–417, 1970.
[7] P. C. Tetlock, “Giving Content to Investor Sentiment: The Role of Media in the Stock Market,” The Journal of Finance, vol. 62, no. 3, pp. 1139–1168, 2007, doi:10.1111/j.1540-6261.2007.01232.x.
[8] W. Antweiler and M. Z. Frank, “Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards,” The Journal of Finance, vol. 59, no. 3, pp. 1259–1294, 2004, doi: 10.1111/j.1540-6261.2004.00662.x.
[9] J. Bollen, H. Mao, and X.-J. Zeng, “Twitter Mood Predicts the Stock Market,” Journal of Computational Science, vol. 2, no. 1, pp. 1–8, 2011, doi: 10.1016/j.jocs.2010.12.007.
[10] T. Fischer and C. Krauss, “Deep Learning with Long Short-Term Memory Networks for Financial Market Predictions,” European Journal of Operational Research, vol. 270, no. 2, pp. 654–669, 2018, doi: 10.1016/j.ejor.2017.11.054.
[11] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proceedings of NAACL-HLT, 2019.
[12] D. Araci, “FinBERT: Financial Sentiment Analysis with Pre-trained Language Models,” arXiv preprint arXiv:1908.10063, 2019.
[13] M. Schuld and F. Petruccione, Supervised Learning with Quantum Computers, Springer, 2018.
[14] V. Havlíček, A. D. Córcoles, K. Temme, et al., “Supervised Learning with Quantum-Enhanced Feature Spaces,” Nature, vol. 567, pp. 209–212, 2019, doi: 10.1038/s41586-019-0980-2.
[15] S. Lloyd, M. Mohseni, and P. Rebentrost, “Quantum Algorithms for Supervised and Unsupervised Machine Learning,” arXiv preprint arXiv:1307.0411, 2013.
[16] R. Orús, S. Mugel, and E. Lizaso, “Quantum Computing for Finance: Overview and Prospects,” Reviews in Physics, vol. 4, 100028, 2019, doi: 10.1016/j.revip.2019.100028.
[17] M. Doosti et al., “A Brief Review of Quantum Machine Learning for Financial Applications,” arXiv preprint, 2024.
[18] H. Abraham, I. Y. Akhalwaya, G. Aleksandrowicz, et al., “Qiskit: An Open-source Framework for Quantum Computing,” Zenodo, 2019, doi: 10.5281/zenodo.2562111.
[19] A. Javadi-Abhari et al., “Quantum Computing with Qiskit,” arXiv preprint arXiv:2405.08810, 2024.
[20] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, 5th ed., Wiley, 2015.
[21] C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
[22] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
[23] S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed., Pearson, 2021.
[24] R. J. Shiller, Irrational Exuberance, 3rd ed., Princeton University Press, 2015.
[25] National Stock Exchange of India (NSE), Historical Index Data / Price–Volume Archives, NSE, Mumbai, India.
