Research PaperNo Access

A Deep Neural Network-Based Multisource Information Fusion Method for Stock Price Prediction of Enterprises

Li Quan

https://orcid.org/0009-0000-4434-521X

Zhengzhou University of Science and Technology, Zhengzhou 450064, P. R. China

E-mail Address: kjxzs360@163.com

Corresponding author.

Search for more papers by this author

and

Dahuan Zheng

https://orcid.org/0009-0007-1382-0672

Zhengzhou University of Science and Technology, Zhengzhou 450064, P. R. China

E-mail Address: dadahuan1122@163.com

Search for more papers by this author

https://doi.org/10.1142/S0218126625500823Cited by:0 (Source: Crossref)

Abstract

Most of the existing research works on stock price prediction ignore the synergistic effect of multi-source factors. Therefore, an enterprise stock price prediction method based on multi-source information fusion based on deep neural network (DNN) is proposed. Based on the condition of multi-source information fusion, the original data related to enterprise stock price are collected from multiple sources, and the structure level and model framework of the method are constructed. The feature extraction technology of DNN is used to extract the features useful for stock price prediction from the pre-processed data, and the DNN structure considering multi-source information fusion is established. Finally, feature extraction and evaluation settings are completed based on data variables. LOSS, ACCURACY and other indicators were used for analysis. The results show that compared with typical prediction methods, this method can make use of multiple information sources more comprehensively and improve the prediction accuracy. In addition, the proposed method has certain flexibility and can adapt to the characteristics and changes of different stock markets

This paper was recommended by Regional Editor Takuro Sato.

Keywords:

References

1. X. Zhu et al., A vehicle-road urban sensing framework for collaborative content delivery in freeway-oriented vehicular networks, IEEE Sens. J. 24(5) (2023) 5662–5674. Crossref, Google Scholar
2. S. Liu et al., Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network, Comput. Commun. 173 (2021) 160–169. Crossref, Web of Science, Google Scholar
3. J. Wang et al., A graph-based approach to multi-source heterogeneous information fusion in stock market, PLos One 17(8) (2022) e0272083. Crossref, Web of Science, Google Scholar
4. F. Wang, K. Yu and L. Yang , Computer graphics algorithm demonstration system, Procedia Comput. Sci. 228 (2023) 736–744. Crossref, Google Scholar
5. Y. Shen et al., A deep learning-based data management scheme for intelligent control of wastewater treatment processes under resource-constrained IoT systems, IEEE Internet Things J. 11 (2024) 25757–25770, https://doi.org/10.1109/JIOT.2024.3388043. Crossref, Web of Science, Google Scholar
6. K. Yu et al., Hybrid quantum classical optimization for low-carbon sustainable edge architecture in RIS-assisted AIoT healthcare systems, IEEE Internet Things J., https://doi.org/10.1109/JIOT.2024.3399234. Web of Science, Google Scholar
7. Q. Zhang et al., Incorporating stock prices and text for stock movement prediction based on information fusion, Eng. Appl. Artif. Intell. 127 (2024) 107377. Crossref, Web of Science, Google Scholar
8. S. Guo, Z. Wang and L. Yang , Design of computer network security storage system based on digital cloud computing technology, 2023 Int. Conf. Evolutionary Algorithms and Soft Computing Techniques (EASCT), IEEE (Bengaluru, India, 2023), pp. 1–7. Crossref, Google Scholar
9. W. C. Huang et al., Attentive gated graph sequence neural network-based time-series information fusion for financial trading, Inf. Fusion 91 (2023) 261–276. Crossref, Web of Science, Google Scholar
10. V. Snášel et al., A generalization of multi-source fusion-based framework to stock selection, Inf. Fusion 102 (2024) 102018. Crossref, Web of Science, Google Scholar
11. S. Ma, S. Xuan and Y. Liang , Analysis model of human resource cross-media fusion based on deep neural network, Comput. Intell. Neurosci. 2022 (2022) 6069589. Web of Science, Google Scholar
12. S. Khan et al., Deep learning-based urban big data fusion in smart cities: Towards traffic monitoring and flow-preserving fusion, Comput. Electr. Eng. 89 (2021) 106906. Crossref, Google Scholar
13. X. Ye et al., A multisource data fusion-based heterogeneous graph attention network for competitor prediction, ACM Trans. Knowl. Discov. Data 18(2) (2023) 1–20. Crossref, Google Scholar
14. S. S. Roy et al., Random forest, gradient boosted machines and deep neural network for stock price forecasting: A comparative analysis on South Korean companies, Int. J. Ad Hoc Ubiq. Comput. 33(1) (2020) 62–71. Crossref, Web of Science, Google Scholar
15. K. Guo and L. Zhang , Multi-source information fusion for safety risk assessment in underground tunnels, Knowl.-Based Syst. 227 (2021) 107210. Crossref, Web of Science, Google Scholar
16. B. Peng et al., An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network, Appl. Intell. 52(3) (2022) 3051–3065. Crossref, Web of Science, Google Scholar
17. S. Li et al., Prediction algorithm of wind waterlogging disaster in distribution network based on multi-source data fusion, Math. Probl. Eng. 2022(1) (2022) 2721734. Google Scholar
18. C. Dadiyala and A. Ambhaikar , A multi-source fused model for stock prediction using technical and linguistic analysis via ensemble learnin, Recent Advances in Material, Manufacturing, and Machine Learning ( CRC Press, 2023), pp. 536–550. Crossref, Google Scholar
19. W. Liu et al., A milling cutter state recognition method based on multi-source heterogeneous data fusion, Int. J. Adv. Manuf. Technol. 122(78) (2022) 3365–3378. Crossref, Google Scholar
20. C. Yang et al., Cloud-edge-device collaboration mechanisms of deep learning models for smart robots in mass personalization, Robot. Comput. — Integr. Manuf. 77 (2022) 102351. Crossref, Web of Science, Google Scholar
21. L. Sun et al., Substation equipment temperature prediction based on multivariate information fusion and deep learning network, PeerJ Comput. Sci. 8 (2022) e1172. Crossref, Web of Science, Google Scholar
22. J. Jiao et al., A comprehensive review on convolutional neural network in machine fault diagnosis, Neurocomputing 417 (2020) 36–63. Crossref, Web of Science, Google Scholar
23. J. Guo et al., A CNN-Bi_LSTM parallel network approach for train travel time prediction, Knowl.-Based Syst. 256 (2022) 109796. Crossref, Web of Science, Google Scholar
24. T. Meng et al., A survey on machine learning for data fusion, Inf. Fusion 57 (2020) 115–129. Crossref, Web of Science, Google Scholar
25. Y. Zhang et al., Information fusion for edge intelligence: A survey, Inf. Fusion 81 (2022) 171–186. Crossref, Web of Science, Google Scholar
26. N. J. Gati et al., Differentially private data fusion and deep learning framework for cyber–physical–social systems: State-of-the-art and perspectives, Inf. Fusion 76 (2021) 298–314. Crossref, Web of Science, Google Scholar
27. C. Tian et al., A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network, Energies 11(12) (2018) 3493. Crossref, Web of Science, Google Scholar
28. J. Xing, W. Wu, Q. Cheng and R. Liu , Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights, Phys. A: Stat. Mech. Appl. 595 (2022) 127079. Crossref, Web of Science, Google Scholar
29. G. Song et al., Stock ranking prediction using a graph aggregation network based on stock price and stock relationship information, Inf. Sci. 643 (2023) 119236. Crossref, Web of Science, Google Scholar
30. D. Jiang and Z. Wang , Research on mechanical equipment fault diagnosis method based on deep learning and information fusion, Sensors 23(15) (2023) 6999. Crossref, Web of Science, Google Scholar
31. Y. Ma et al., Real-time driving behavior identification based on multi-source data fusion, Int. J. Environ. Res. Public Health 19(1) (2021) 348. Crossref, Google Scholar