LSTM stock prediction model based on blockchain

Abstract

The stock market is a vital component of the financial sector. Due to the inherent uncertainty and volatility of the stock market, stock price prediction has always been both intriguing and challenging. To improve the accuracy of stock predictions, we construct a model that integrates investor sentiment with Long Short-Term Memory (LSTM) networks. By extracting sentiment data from the “Financial Post” and quantifying it with the Vader sentiment lexicon, we add a sentiment index to improve stock price forecasting. We combine sentiment factors with traditional trading indicators, making predictions more accurate. Furthermore, we deploy our system on the blockchain to enhance data security, reduce the risk of malicious attacks, and improve system robustness. This integration of sentiment analysis and blockchain offers a novel approach to stock market predictions, providing secure and reliable decision support for investors and financial institutions. We deploy our system and demonstrate that our system is both efficient and practical. For 312 bytes of stock data, we achieve a latency of 434.42 ms with one node and 565.69 ms with five nodes. For 1700 bytes of sentiment data, we achieve a latency of 1405.25 ms with one node and 1750.25 ms with five nodes.

Keywords

Blockchains / Long Short-Term Memory (LSTM) / Stock price prediction / Sentiment analysis

CRediT authorship contribution statement

Yongdan Wang: Writing - review & editing, Writing - original draft, Methodology, Formal analysis. Haibin Zhang: Writing - original draft, Project administration, Funding acquisition, Conceptualization. Baohan Huang: Validation, Formal analysis, Data curation. Zhijun Lin: Resources, Writing - review & editing. Chuan Pang: Resources, Writing - review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This paper was funded in part by NSFC (62272043) and funded by Yangtze Delta Region Institute of Tsinghua University, Zhejiang (LZZLX24F007).

References

[1] J.J. Siegel, Stocks for the Long Run: The Definitive Guide to Financial Market Returns & Long-Term Investment Strategies, McGraw-Hill Education, 2021.

[2] S. Prasanna, D. Ezhilmaran, An analysis on stock market prediction using data mining techniques, Int. J. Comput. Sci. Eng. Technol. (IJCSET) 4 (3) (2013) 49-51.

[3] D.P. Gandhmal, K. Kumar, Systematic analysis and review of stock market prediction techniques, Comput. Sci. Rev. 34 (2019) 100190.

[4] D. Shah, H. Isah, F. Zulkernine, Stock market analysis: A review and taxonomy of prediction techniques, Int. J. Financ. Stud. 7 (2) (2019) 26.

[5] O.B. Sezer, M.U. Gudelek, A.M. Ozbayoglu, Financial time series forecasting with deep learning: A systematic literature review: 2005-2019, Appl. Soft Comput. 90 (2020) 106181.

[6] S. Duan, H. Zhang, X. Sui, B. Huang, C. Mu, G. Di, X. Wang, Dashing and star: Byzantine fault tolerance with weak certificates,in:Proceedings of the Nineteenth European Conference on Computer Systems, 2024, pp. 250-264.

[7] S. Duan, H. Zhang, Recent progress on BFT in the era of blockchains, Natl. Sci. Rev. 9 (10) (2022) nwac132.

[8] X. Wang, S. Duan, J. Clavin, H. Zhang, Bft in blockchains: From protocols to use cases, ACM Comput. Surv. 54 (10s) (2022) 1-37.

[9] A.T. Sherman, F. Javani, H. Zhang, E. Golaszewski, On the origins and variations of blockchain technologies, IEEE Secur. Priv. 17 (1) (2019) 72-77.

[10] S. Dara, P. Tumma, Feature extraction by using deep learning: A survey, in: 2018 Second International Conference on Electronics, Communication and Aerospace Technology, ICECA, IEEE, 2018, pp. 1795-1801.

[11] S.M. Al-Selwi, M.F. Hassan, S.J. Abdulkadir, A. Muneer, E.H. Sumiea, A. Alqushaibi, M.G. Ragab, RNN-LSTM: From applications to modeling techniques and beyond—Systematic review, J. King Saud Univ. Comput. Inf. Sci. (2024) 102068.

[12] T. Fischer, C. Krauss, Deep learning with long short-term memory networks for financial market predictions, European J. Oper. Res. 270 (2) (2018) 654-669.

[13] H.N. Bhandari, B. Rimal, N.R. Pokhrel, R. Rimal, K.R. Dahal, R.K. Khatri, Predicting stock market index using LSTM, Mach. Learn. Appl. 9 (2022) 100320.

[14] T.H. Nguyen, K. Shirai, J. Velcin, Sentiment analysis on social media for stock movement prediction, Expert Syst. Appl. 42 (24) (2015) 9603-9611.

[15] N. Rane, S. Choudhary, J. Rane, Blockchain and artificial intelligence (AI) integration for revolutionizing security and transparency in finance, 2023, Available at SSRN 4644253.

[16] M. Pilkington, Blockchain technology: principles and applications, in: Research Handbook on Digital Transformations, Edward Elgar Publishing, 2016, pp. 225-253.

[17] K. Zhang, H.-A. Jacobsen, Towards dependable, scalable, and pervasive distributed ledgers with blockchains, in: ICDCS, 2018, pp. 1337-1346.

[18] C. Liu, D. Wang, D. Li, S. Guo, W. Li, X. Qiu, Trusted access control mechanism for data with blockchain-assisted attribute encryption, High Confid. Comput. (2024) 100265.

[19] N. Upadhyay, Demystifying blockchain: A critical analysis of challenges, applications and opportunities, Int. J. Inf. Manage. 54 (2020) 102120.

[20] Y. He, Z. Zhou, Y. Pan, F. Chong, B. Wu, K. Xiao, H. Li, Review of data security within energy blockchain: A comprehensive analysis of storage, management, and utilization, High Confid. Comput. (2024) 100233.

[21] H. Min, Blockchain technology for enhancing supply chain resilience, Bus. Horiz. 62 (1) (2019) 35-45.

[22] Y. Chen, H. Chen, Y. Zhang, M. Han, M. Siddula, Z. Cai, A survey on blockchain systems: Attacks, defenses, and privacy preservation, High Confid. Comput. 2 (2) (2022) 100048.

[23] Y. Su, C.-C.J. Kuo, et al., Recurrent neural networks and their memory behavior: A survey, APSIPA Trans. Signal Inf. Process. 11 (1) (2022).

[24] I.D. Mienye, T.G. Swart, G. Obaido, Recurrent neural networks: A comprehensive review of architectures, variants, and applications, Information 15 (9) (2024) 517.

[25] S. Siami-Namini, A.S. Namin, Forecasting economics and financial time series: ARIMA vs. LSTM, 2018, arXiv preprint arXiv:1803.06386.

[26] G. Bathla, Stock price prediction using LSTM and SVR, in: 2020 Sixth International Conference on Parallel, Distributed and Grid Computing, PDGC, IEEE, 2020, pp. 211-214.

[27] W. Buachuen, P. Kantavat, Automated stock trading system using technical analysis and deep learning models, in:Proceedings of the 13th International Conference on Advances in Information Technology, 2023, pp. 1-9.

[28] J.M.-T. Wu, Z. Li, N. Herencsar, B. Vo, J.C.-W. Lin, A graph-based CNN-LSTM stock price prediction algorithm with leading indicators, Multimedia Syst. 29 (3) (2023) 1751-1770.

[29] L. Tian, L. Feng, L. Yang, Y. Guo, Stock price prediction based on LSTM and LightGBM hybrid model, J. Supercomput. 78 (9) (2022) 11768-11793.

[30] S. Chen, C. Zhou, Stock prediction based on genetic algorithm feature selection and long short-term memory neural network, IEEE Access 9 (2020) 9066-9072.

[31] Z. Jin, Y. Yang, Y. Liu, Stock closing price prediction based on sentiment analysis and LSTM, Neural Comput. Appl. 32 (2020) 9713-9729.

[32] X. Ji, J. Wang, Z. Yan, A stock price prediction method based on deep learning technology, Int. J. Crowd Sci. 5 (1) (2021) 55-72.

[33] W. Gao, W.G. Hatcher, W. Yu, A survey of blockchain: Techniques, applications, and challenges, in: 2018 27th International Conference on Computer Communication and Networks, ICCCN, IEEE, 2018, pp. 1-11.

[34] S. Dong, K. Abbas, M. Li, J. Kamruzzaman, Blockchain technology and application: an overview, PeerJ Comput. Sci. 9 (2023) e1705.

[35] R. Khatwani, M. Mishra, M. Bedarkar, K. Nair, J. Mistry, Impact of blockchain on financial technology innovation in the banking, financial services and insurance (BFSI) sector, J. Stat. Appl. Probab. 12 (1) (2023) 181-189.

[36] J.-A. Tarr, Distributed ledger technology, blockchain and insurance: Opportunities, risks and challenges, Insur. Law J. 29 (3) (2018) 254-268.

[37] V. Gatteschi, F. Lamberti, C. Demartini, C. Pranteda, V. Santamaría, Blockchain and smart contracts for insurance: Is the technology mature enough? Futur. Internet 10 (2) (2018) 20.

[38] O. Bhadra, S. Sahoo, C.M. Kumar, R. Halder, Decentralized insurance subrogation using blockchain, in:Proceedings of the 2022 5th International Conference on Blockchain Technology and Applications, 2022, pp. 1-9.

[39] L. Zhou, L. Wang, Y. Sun, MIStore: a blockchain-based medical insurance storage system, J. Med. Syst. 42 (8) (2018) 149.

[40] T. Lepoint, G. Ciocarlie, K. Eldefrawy, Blockcis—a blockchain-based cyber insurance system, in: 2018 IEEE International Conference on Cloud Engineering, IC2E, IEEE, 2018, pp. 378-384.

[41] F. Loukil, K. Boukadi, R. Hussain, M. Abed, Ciosy: A collaborative blockchain-based insurance system, Electronics 10 (11) (2021) 1343.

[42] H. Kantur, C. Bamuleseyo, How smart contracts can change the insurance industry: Benefits and challenges of using blockchain technology, 2018.

[43] T.O. Sanyaolu, A.G. Adeleke, C.F. Azubuko, O.S. Osundare, Harnessing blockchain technology in banking to enhance financial inclusion, security, and transaction efficiency, Int. J. Sch. Res. Sci. Technol. August 5 (01) (2024) 035-053.

[44] L. Rella, Blockchain technologies and remittances: From financial inclusion to correspondent banking, Front. Blockchain 2 (2019) 14.

[45] D. Jayasuriya Daluwathumullagamage, A. Sims, Fantastic beasts: blockchain based banking, J. Risk Financ. Manag. 14 (4) (2021) 170.

[46] M. Mamun, B. Arifuzzaman, S.N. Mahmud, Banking management system architecture using AI & blockchain, in:Proceedings of the 2nd International Conference on Computing Advancements, 2022, pp. 75-82.

[47] Z. Xu, Q. Wang, Z. Wang, D. Liu, Y. Xiang, S. Wen, PPM: a provenance-provided data sharing model for open banking via blockchain,in:Proceedings of the Australasian Computer Science Week Multiconference, 2020, pp. 1-8.

[48] A. Kumari, N.C. Devi, The impact of fintech and blockchain technologies on banking and financial services, Technol. Innov. Manag. Rev. 12 (1/2) (2022) 1-11.

[49] M. Javaid, A. Haleem, R.P. Singh, R. Suman, S. Khan, A review of blockchain technology applications for financial services, BenchCouncil Trans. Benchmarks Stand. Eval. 2 (3) (2022) 100073.

[50] V. Unadkat, P. Sayani, P. Kanani, P. Doshi, Deep learning for financial prediction, in: 2018 International Conference on Circuits and Systems in Digital Enterprise Technology, ICCSDET, IEEE, 2018, pp. 1-6.

[51] C. Hutto, E. Gilbert, Vader: A parsimonious rule-based model for sentiment analysis of social media text,in:Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, no. 1, 2014, pp. 216-225.

[52] S. Al-Natour, O. Turetken, A comparative assessment of sentiment analysis and star ratings for consumer reviews, Int. J. Inf. Manage. 54 (2020) 102132.

[53] M. Castro, B. Liskov, et al., Practical byzantine fault tolerance, in: OsDI, vol. 99, no. 1999, 1999, pp. 173-186.

[54] J.A. Buchmann, E. Karatsiolis, A. Wiesmaier, Introduction to Public Key Infrastructures, Springer Science & Business Media, 2013.

[55] R. Oppliger, SSL and TLS: Theory and Practice, Artech House, 2023.