Knowledge Graph Construction for the U.S. Stock Market: A Statistical Learning and Risk Management Approach

Wei Yang; Jincan Duan

doi:10.70393/6a6374616d.323439

OPEN ACCESS |Research Article ||1 January 2025

Knowledge Graph Construction for the U.S. Stock Market: A Statistical Learning and Risk Management Approach

Wei Yang

Wuhan University

wyangstar@whu.edu.cn

Economics and Management School, Wuhan University, Wuhan, China, 430072, wyangstar@whu.edu.cn.

Jincan Duan

Xiamen University

jasmine.djc@gmail.com

School of Management, Xiamen University, Xiamen, China, 361005，jasmine.djc@gmail.com.

* Corresponding Author¹: Wei Yang, E-Mail: wyangstar@whu.edu.cn

Publication

Accepted Unknow ; Published 2025 January 1

Journal of Computer Technology and Applied Mathematics, 2025, 2(1), 3007-4126.

Abstract

This paper explores the integration of dynamic knowledge graphs (DKGs) and advanced AI techniques, such as large language models (LLMs) and graph neural networks (GNNs), for enhancing financial market analysis and risk management. By developing the Integrated Context Knowledge Graph Generator (ICKG) and the Financial Dynamic Knowledge Graph (FinDKG), the study demonstrates how these models can predict market trends, optimize investment strategies, and improve risk mitigation. The results highlight the superior performance of the KGTransformer model in link prediction tasks, showcasing its potential for more accurate and insightful financial decision-making.

Keywords

Dynamic Knowledge Graphs (DKGs) , Large Language Models (LLMs) , Financial Market Analysis , Risk Management .

Metadata

DOI:

10.70393/6a6374616d.323439

ARK:

ark:/40704/JCTAM.v2n1a01

Pages: 1-7

References: 39

Disciplines: Artificial Intelligence

Subjects: Statistical Analysis

Cite This Article

APA Style

Yang, W. & Duan, J. (2025). Knowledge graph construction for the u.s. stock market: a statistical learning and risk management approach. Journal of Computer Technology and Applied Mathematics, 2(1), 1-7. https://doi.org/10.70393/6a6374616d.323439

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

References

1.

Jareño, Francisco, and Loredana Negrut. "US stock market and macroeconomic factors." Journal of Applied Business Research 32.1 (2016): 325.

2.

Diao, S., Wan, Y., Huang, S., & Ma, H. (2024, July). Research on Cancer Prediction and Identification based on Multimodal Medical Image Fusion. In Proceedings of the 2024 3rd International Symposium on Robotics, Artificial Intelligence and Information Engineering (pp. 120-124).

3.

White, C. B. (2000). What P/E will the US stock market support?. Financial Analysts Journal, 56(6), 30-38.

4.

Diao, S., Wei, C., Wang, J., & Li, Y. (2024). Ventilator pressure prediction using recurrent neural network. arXiv preprint arXiv:2410.06552.

5.

Feng, Z., Ge, M., & Meng, Q. (2024). Enhancing Energy Efficiency in Green Buildings Through Artificial Intelligence.

6.

7.

Ge, Minyue, Zhang Feng, and Qian Meng. "Urban Planning and Green Building Technologies Based on Artificial Intelligence: Principles, Applications, and Global Case Study Analysis."

8.

Huang, S., Diao, S., Wan, Y., & Song, C. (2024, August). Research on Multi-agency Collaboration Medical Images Analysis and Classification System based on Federated Learning. In Proceedings of the 2024 International Conference on Biomedicine and Intelligent Technology (pp. 40-44).

9.

Huang, S., Diao, S., & Wan, Y. (2024, September). Application of machine learning methods in predicting functional recovery in ischemic stroke patients. In The 1st International scientific and practical conference “Innovative scientific research: theory, methodology, practice”(September 03–06, 2024) Boston, USA. International Science Group. 2024. 289 p. (p. 240).

10.

Wang, D. (Ed.). (2016). Information Science and Electronic Engineering: Proceedings of the 3rd International Conference of Electronic Engineering and Information Science (ICEEIS 2016), January 4-5, 2016, Harbin, China. CRC Press.

11.

Diao, S., Huang, S., & Wan, Y. (2024, September). Early detection of cervical adenocarcinoma using immunohistochemical staining patterns analyzed through computer vision technology. In The 1st International scientific and practical conference “Innovative scientific research: theory, methodology, practice”(September 03–06, 2024) Boston, USA. International Science Group. 2024. 289 p. (p. 256).

12.

Gong, Y., Zhang, Q., Zheng, H., Liu, Z., & Chen, S. (2024, September). Graphical Structural Learning of rs-fMRI data in Heavy Smokers. In 2024 4th International Conference on Computer Science and Blockchain (CCSB) (pp. 434-438). IEEE.

13.

Huang, S., Diao, S., Zhao, H., & Xu, L. (2024, June). The Contribution of Federated Learning to AI Development. In The 24th International scientific and practical conference “Technologies of scientists and implementation of modern methods”(June 18–21, 2024) Copenhagen, Denmark. International Science Group. 2024. 431 p. (p. 358).

14.

Chen, J., Xu, W., Ding, Z., Xu, J., Yan, H., & Zhang, X. (2024). Advancing Prompt Recovery in NLP: A Deep Dive into the Integration of Gemma-2b-it and Phi2 Models. arXiv preprint arXiv:2407.05233.

15.

J. Chen, J. Xiao, and W. Xu, “A hybrid stacking method for short-term price forecasting in electricity trading market,” in 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), IEEE, 2024, pp. 1–5.

16.

Feng, Z., Ge, M., Meng, Q., & Chen, Y. (2024). Research on Old Building Renovation Strategies by using Green Building Technologies.

17.

Hall, R. E. (2001). Struggling to understand the stock market. American Economic Review, 91(2), 1-11.

18.

Huang, S., Liang, Y., Shen, F., & Gao, F. (2024, July). Research on Federated Learning's Contribution to Trustworthy and Responsible Artificial Intelligence. In Proceedings of the 2024 3rd International Symposium on Robotics, Artificial Intelligence and Information Engineering (pp. 125-129).

19.

Mishkin, Frederic S., and Eugene N. White. "US stock market crashes and their aftermath: implications for monetary policy." (2002).

20.

Li, B., Jiang, G., Li, N., & Song, C. (2024, August). Research on large-scale structured and unstructured data processing based on large language model. In Proceedings of the International Conference on Machine Learning, Pattern Recognition and Automation Engineering (pp. 111-116).

21.

Wang, Y., He, Z., Zou, J., Xie, H., & Bao, J. (2024). Energy Transition for Sustainable Economy: What is the Role of Government Governance and Public Concern?. Finance Research Letters, 106087.

22.

Jung, J., & Shiller, R. J. (2005). Samuelson's dictum and the stock market. Economic Inquiry, 43(2), 221-228.

23.

Diao, Su, Danyi Huang, and Gaozhe Jiang. "The Role of Artificial Intelligence in Personalized Medicine through Advanced Imaging." (2024).

24.

Kilian, Lutz, and Cheolbeom Park. "The impact of oil price shocks on the US stock market." International economic review 50.4 (2009): 1267-1287.

25.

Li, B., Zhang, K., Sun, Y., & Zou, J. (2024). Research on Travel Route Planning Optimization based on Large Language Model.

26.

Liu, Z., Jian, X., Sadiq, T., Shaikh, Z. A., Alfarraj, O., Alblehai, F., & Tolba, A. (2024). Promoted Osprey Optimizer: a solution for ORPD problem with electric vehicle penetration. Scientific Reports, 14(1), 28052.

27.

Wang, L., Chen, M., Chen, G., Luo, T., & Liu, F. (2023). Loading capacity prediction of the auxetic tubular lattice structures by multiscale shakedown analysis. Composite Structures, 314, 116938.

28.

Wang, L., He, L., Liu, F., Yuan, H., Li, J., & Chen, M. (2024). Mechanical Characterization of Multifunctional Metal-Coated Polymer Lattice Structures. Materials, 17(3), 741.

29.

Humpe, Andreas, and Peter Macmillan. "Can macroeconomic variables explain long-term stock market movements? A comparison of the US and Japan." Applied financial economics 19.2 (2009): 111-

30.

Xiao, J., Deng, T., & Bi, S. (2024). Comparative Analysis of LSTM, GRU, and Transformer Models for Stock Price Prediction. arXiv preprint arXiv:2411.05790.

31.

Wang, L., He, L., Wang, X., Soleimanian, S., Yu, Y., Chen, G., ... & Chen, M. (2023). Multiscale Evaluation of Mechanical Properties for Metal-Coated Lattice Structures. Chinese Journal of Mechanical Engineering, 36(1), 106.

32.

Wang, L., Zhang, Z., Chen, M., Xie, J., Liu, F., Yuan, H., ... & Yu, L. (2023, October). Machine Learning-Based Fatigue Life Evaluation of the Pump Spindle Assembly With Parametrized Geometry. In ASME International Mechanical Engineering Congress and Exposition (Vol. 87684, p. V011T12A022). American Society of Mechanical Engineers.

33.

Laopodis, N. T. (2009). Fiscal policy and stock market efficiency: Evidence for the United States. The quarterly Review of Economics and finance, 49(2), 633-650.

34.

Xu, Q., Xu, L., Jiang, G., & He, Y. (2024, June). Artificial Intelligence in Risk Protection for Financial Payment Systems. In The 24th International scientific and practical conference “Technologies of scientists and implementation of modern methods”(June 18–21, 2024) Copenhagen, Denmark. International Science Group. 2024. 431 p. (p. 344).

35.

Yang, J., Tian, K., Zhao, H., Feng, Z., Bourouis, S., Dhahbi, S., ... & Por, L. Y. (2024). Wastewater treatment monitoring: Fault detection in sensors using transductive learning and improved reinforcement learning. Expert Systems with Applications, 125805.

36.

Zhang, X., Xu, L., Li, N., & Zou, J. (2024). Research on Credit Risk Assessment Optimization based on Machine Learning.

37.

Zheng, H., Zhang, Q., Gong, Y., Liu, Z., & Chen, S. (2024, September). Identification of prognostic biomarkers for stage iii non-small cell lung carcinoma in female nonsmokers using machine learning. In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE) (pp. 323-326). IEEE.

38.

Serletis, Apostolos, and Aryeh Adam Rosenberg. "Mean reversion in the US stock market." Chaos, Solitons & Fractals40.4 (2009): 2007-2015.

39.

W. Xu, J. Xiao, and J. Chen, “Leveraging large language models to enhance personalized recommendations in e-commerce,” arXiv, arXiv:2410.12829, 2024.

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