IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i7p2795-d340191.html
   My bibliography  Save this article

Research on the Knowledge Association Reasoning of Financial Reports Based on a Graph Network

Author

Listed:
  • Zhuoqian Liang

    (School of Management, Jinan University, Guangzhou 510632, China)

  • Ding Pan

    (School of Management, Jinan University, Guangzhou 510632, China)

  • Yuan Deng

    (School of Accounting and Auditing, Guangxi University of Finance and Economics, Nanning 530007, China)

Abstract

With increasingly strict supervision, the complexity of enterprises’ annual reports has increased significantly, and the size of the text corpus has grown at an enormous rate. Information fusion for financial reporting has become a research hotspot. The difficulty of this problem is in filtering the massive amount of heterogeneous data and integrating related information distributed in different locations according to decision topics. This paper proposes a Graph NetWork (GNW) model that establishes the overall connection between decentralized information, as well as a graph network generation algorithm to filter large and complex data sets in financial reports and to mine key information to make it suitable for different decision situations. Finally, this paper uses the Planar Maximally Filtered Graph (PMFG) as a benchmark to show the effect of the generation algorithm.

Suggested Citation

  • Zhuoqian Liang & Ding Pan & Yuan Deng, 2020. "Research on the Knowledge Association Reasoning of Financial Reports Based on a Graph Network," Sustainability, MDPI, vol. 12(7), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2795-:d:340191
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/7/2795/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/7/2795/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hamzeh Khalili & David Rincón & Sebastià Sallent & José Ramón Piney, 2020. "An Energy-Efficient Distributed Dynamic Bandwidth Allocation Algorithm for Passive Optical Access Networks," Sustainability, MDPI, vol. 12(6), pages 1-20, March.
    2. T. Di Matteo & F. Pozzi & T. Aste, 2010. "The use of dynamical networks to detect the hierarchical organization of financial market sectors," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 73(1), pages 3-11, January.
    3. Chou, Chi-Chun & Hwang, Nen-Chen Richard & Wang, Tawei & Debreceny, Roger, 2018. "The topical link model-integrating topic-centric information in XBRL-formatted reports," International Journal of Accounting Information Systems, Elsevier, vol. 29(C), pages 16-36.
    4. Musmeci, Nicoló & Nicosia, Vincenzo & Aste, Tomaso & Di Matteo, Tiziana & Latora, Vito, 2017. "The multiplex dependency structure of financial markets," LSE Research Online Documents on Economics 85337, London School of Economics and Political Science, LSE Library.
    5. Angela K. Davis & Isho Tama†Sweet, 2012. "Managers’ Use of Language Across Alternative Disclosure Outlets: Earnings Press Releases versus MD&A," Contemporary Accounting Research, John Wiley & Sons, vol. 29(3), pages 804-837, September.
    6. Nicolò Musmeci & Vincenzo Nicosia & Tomaso Aste & Tiziana Di Matteo & Vito Latora, 2017. "The Multiplex Dependency Structure of Financial Markets," Complexity, Hindawi, vol. 2017, pages 1-13, September.
    7. Chou, Chi-Chun & Chang, C. Janie & Peng, Jacob, 2016. "Integrating XBRL data with textual information in Chinese: A semantic web approach," International Journal of Accounting Information Systems, Elsevier, vol. 21(C), pages 32-46.
    8. Yuexiang Yang & Xiaoyu Zheng & Zhen Sun, 2020. "Coal Resource Security Assessment in China: A Study Using Entropy-Weight-Based TOPSIS and BP Neural Network," Sustainability, MDPI, vol. 12(6), pages 1-15, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Khomami, Mohammad Mehdi Daliri & Meybodi, Mohammad Reza & Rezvanian, Alireza, 2024. "Exploring social networks through stochastic multilayer graph modeling," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    2. Gong, Jue & Wang, Gang-Jin & Zhou, Yang & Zhu, You & Xie, Chi & Foglia, Matteo, 2023. "Spreading of cross-market volatility information: Evidence from multiplex network analysis of volatility spillovers," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 83(C).
    3. Dai, Zhifeng & Tang, Rui & Zhang, Xiaotong, 2023. "A new multilayer network for measuring interconnectedness among the energy firms," Energy Economics, Elsevier, vol. 124(C).
    4. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    5. Giuseppe Brandi & Ruggero Gramatica & Tiziana Di Matteo, 2019. "Unveil stock correlation via a new tensor-based decomposition method," Papers 1911.06126, arXiv.org, revised Apr 2020.
    6. Dai, Zhifeng & Tang, Rui & Zhang, Xinhua, 2023. "Multilayer network analysis for measuring the inter-connectedness between the oil market and G20 stock markets," Energy Economics, Elsevier, vol. 120(C).
    7. Nava, Noemi & Di Matteo, T. & Aste, Tomaso, 2018. "Dynamic correlations at different time-scales with empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 534-544.
    8. Matteo Foglia & Vasilios Plakandaras & Rangan Gupta & Qiang Ji, 2024. "Long-Span Multi-Layer Spillovers between Moments of Advanced Equity Markets: The Role of Climate Risks," Working Papers 202415, University of Pretoria, Department of Economics.
    9. Yong Tang & Jason Jie Xiong & Zi-Yang Jia & Yi-Cheng Zhang, 2018. "Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets," Complexity, Hindawi, vol. 2018, pages 1-31, November.
    10. Krishnagopal, Sanjukta & Bianconi, Ginestra, 2023. "Topology and dynamics of higher-order multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    11. Giuseppe Brandi & T. Di Matteo, 2020. "A new multilayer network construction via Tensor learning," Papers 2004.05367, arXiv.org.
    12. Pang, Raymond Ka-Kay & Granados, Oscar M. & Chhajer, Harsh & Legara, Erika Fille T., 2021. "An analysis of network filtering methods to sovereign bond yields during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    13. Fábio Albuquerque & Paula Gomes Dos Santos, 2023. "Recent Trends in Accounting and Information System Research: A Literature Review Using Textual Analysis Tools," FinTech, MDPI, vol. 2(2), pages 1-27, April.
    14. Douglas Castilho & Tharsis T. P. Souza & Soong Moon Kang & Jo~ao Gama & Andr'e C. P. L. F. de Carvalho, 2021. "Forecasting Financial Market Structure from Network Features using Machine Learning," Papers 2110.11751, arXiv.org.
    15. Sharifah Milda Amirul & Noor Ismawati Jaafar & Anna Azriati Che Azmi, 2022. "Two decades of XBRL: a science mapping of research trends and future research agenda," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2301-2324, August.
    16. Millington, Tristan & Niranjan, Mahesan, 2021. "Construction of minimum spanning trees from financial returns using rank correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    17. Xie, Yiwei & Jiao, Feng & Li, Shihan & Liu, Qingfu & Tse, Yiuman, 2022. "Systemic risk in financial institutions: A multiplex network approach," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    18. Wang, Gang-Jin & Xiong, Lu & Zhu, You & Xie, Chi & Foglia, Matteo, 2022. "Multilayer network analysis of investor sentiment and stock returns," Research in International Business and Finance, Elsevier, vol. 62(C).
    19. Liu, Peipei & Huang, Wei-Qiang, 2022. "Modelling international sovereign risk information spillovers: A multilayer network approach," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    20. Ahelegbey, Daniel Felix & Giudici, Paolo & Hadji-Misheva, Branka, 2019. "Latent factor models for credit scoring in P2P systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 112-121.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2795-:d:340191. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.