IDEAS home Printed from https://ideas.repec.org/a/now/fntacc/1400000070.html
   My bibliography  Save this article

Bookkeeping Graphs: Computational Theory and Applications

Author

Listed:
  • Pierre Jinghong Liang

Abstract

This monograph first describes the graph or network representation of Double-Entry bookkeeping both in theory and in practice. The representation serves as the intellectual basis for a series of applied computational works on pattern recognition and anomaly detection in corporate journal-entry audit settings. The second part of the monograph reviews the computational theory of pattern recognition and anomaly detection built on the Minimum Description Length (MDL) principle. The main part of the monograph describes how the computational MDL theory is applied to recognize patterns and detect anomalous transactions in graphs representing the journal entries of a large set of transactions extracted from real-world corporate entities’ bookkeeping data.

Suggested Citation

  • Pierre Jinghong Liang, 2023. "Bookkeeping Graphs: Computational Theory and Applications," Foundations and Trends(R) in Accounting, now publishers, vol. 17(2), pages 77-172, April.
    • Handle: RePEc:now:fntacc:1400000070
    DOI: 10.1561/1400000070
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1561/1400000070
    Download Restriction: no
    File URL: https://libkey.io/10.1561/1400000070?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Xuemin (Sterling) Yan & Lingling Zheng, 2017. "Fundamental Analysis and the Cross-Section of Stock Returns: A Data-Mining Approach," The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1382-1423.
    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. Andrew Y Chen & Tom Zimmermann & Jeffrey Pontiff, 2020. "Publication Bias and the Cross-Section of Stock Returns," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(2), pages 249-289.
    2. Tomoshiro Ochiai & Jose C. Nacher, 2020. "Unveiling the directional network behind the financial statements data using volatility constraint correlation," Papers 2008.07836, arXiv.org, revised Jun 2023.
    3. Meng, Yongqiang & Shen, Dehua & Xiong, Xiong, 2023. "When stock price crash risk meets fundamentals," Research in International Business and Finance, Elsevier, vol. 65(C).
    4. Jérôme Dugast & Thierry Foucault, 2020. "Equilibrium Data Mining and Data Abundance," Post-Print hal-02933315, HAL.
    5. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
    6. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
    7. Pawel Dlotko & Wanling Qiu & Simon Rudkin, 2019. "Financial ratios and stock returns reappraised through a topological data analysis lens," Papers 1911.10297, arXiv.org.
    8. Andrew Y. Chen & Tom Zimmermann, 2022. "Publication Bias in Asset Pricing Research," Papers 2209.13623, arXiv.org, revised Sep 2023.
    9. Fernando Moraes & Rodrigo De-Losso, 2020. "Risk Factor Centrality and the Cross-Section of Expected Returns," Working Papers, Department of Economics 2020_17, University of São Paulo (FEA-USP).
    10. Zhu, Zhaobo & Sun, Licheng & Yung, Kenneth, 2020. "Fundamental strength strategy: The role of investor sentiment versus limits to arbitrage," International Review of Financial Analysis, Elsevier, vol. 71(C).
    11. Doron Avramov & Guy Kaplanski & Avanidhar Subrahmanyam, 2022. "Postfundamentals Price Drift in Capital Markets: A Regression Regularization Perspective," Management Science, INFORMS, vol. 68(10), pages 7658-7681, October.
    12. Constantinos Kardaras & Hyeng Keun Koo & Johannes Ruf, 2022. "Estimation of growth in fund models," Papers 2208.02573, arXiv.org.
    13. Chang, Danting, 2021. "Fundamental anomalies and the size puzzle in China: A data mining approach," Finance Research Letters, Elsevier, vol. 42(C).
    14. L.J. Basson & Sune Ferreira-Schenk & Zandri Dickason-Koekemoer, 2022. "Fractal Dimension Option Hedging Strategy Implementation During Turbulent Market Conditions in Developing and Developed Countries," International Journal of Economics and Financial Issues, Econjournals, vol. 12(2), pages 84-95, March.
    15. Zhu, Zhaobo & Ji, Qiang & Sun, Licheng & Zhai, Pengxiang, 2020. "Oil price shocks, investor sentiment, and asset pricing anomalies in the oil and gas industry," International Review of Financial Analysis, Elsevier, vol. 70(C).
    16. Hollstein, Fabian, 2022. "The world of anomalies: Smaller than we think?," Journal of International Money and Finance, Elsevier, vol. 129(C).
    17. Cakici, Nusret & Zaremba, Adam & Bianchi, Robert J. & Pham, Nga, 2021. "False discoveries in the anomaly research: New insights from the Stock Exchange of Melbourne (1927–1987)," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
    18. Kewei Hou & Chen Xue & Lu Zhang, 2017. "Replicating Anomalies," NBER Working Papers 23394, National Bureau of Economic Research, Inc.
    19. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    20. Zhu, Zhaobo & Sun, Licheng & Yung, Kenneth & Chen, Min, 2020. "Limited investor attention, relative fundamental strength, and the cross-section of stock returns," The British Accounting Review, Elsevier, vol. 52(4).

    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:now:fntacc:1400000070. 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: Lucy Wiseman (email available below). General contact details of provider: http://www.nowpublishers.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.