IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v8y2020i1p2-d308832.html
   My bibliography  Save this article

Research on Audit Supervision of Internet Finance

Author

Listed:
  • Hua Liu

    (School of Finance, Nanjing Audit University, Nanjing 211815, China)

  • Sheng Ge

    (School of International Education, Nanjing Audit University, Nanjing 211815, China)

Abstract

Internet finance is a new form of finance that applies capacities found on the Internet to the traditional financial industry. However, at the present stage, internet finance is faced with many problems, such as overly rapid development and non-standard operation. This paper adopted the evolutionary game theory as the analysis tool to design an evolutionary game model of government audit supervision of Internet finance, and analyzed the evolutionary stability of the strategies used by Internet financial institutions and government financial audit supervision departments. A simulation calculation was carried out by placing the calculation experimental method “Scenario–Coping”, which simulated the initial probability of different strategies adopted by both parties of the game and evaluated the influence of changing the penalty intensity of Internet financial institutions’ violation on the outcome of the evolutionary game. Based on the simulation analysis, the paper provided policy suggestions on strengthening audit supervision and promoting its sustainable development from three aspects: strengthening the construction of the Internet financial credit information system, improving Internet financial laws and regulations, and improving the early warning level of Internet financial credit risk.

Suggested Citation

  • Hua Liu & Sheng Ge, 2020. "Research on Audit Supervision of Internet Finance," IJFS, MDPI, vol. 8(1), pages 1-15, January.
    • Handle: RePEc:gam:jijfss:v:8:y:2020:i:1:p:2-:d:308832
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/8/1/2/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7072/8/1/2/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Economides, Nicholas, 2001. "The impact of the Internet on financial markets," Journal of Financial Transformation, Capco Institute, vol. 1, pages 8-13.
    2. W. Brian Arthur & Paul Tayler, "undated". "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Computing in Economics and Finance 1997 57, Society for Computational Economics.
    3. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
    4. Levy, Haim & Levy, Moshe & Solomon, Sorin, 2000. "Microscopic Simulation of Financial Markets," Elsevier Monographs, Elsevier, edition 1, number 9780124458901.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Huishui Su & Yu Lu & Oleksii Lyulyov & Tetyana Pimonenko, 2023. "Good Governance within Public Participation and National Audit for Reducing Corruption," Sustainability, MDPI, vol. 15(9), pages 1-17, April.
    2. Huntington, Heather & Stevens, Caleb, 2023. "Taking stock of global land indicators: A comparative analysis of approaches for a globally consistent land tenure security measure," Land Use Policy, Elsevier, vol. 124(C).
    3. Guangyu Mu & Yuhan Wang & Nan Gao & Xiurong Li, 2023. "A Novel Tripartite Evolutionary Game Model for Internet Consumer Financial Regulation," SAGE Open, , vol. 13(3), pages 21582440231, August.
    4. Hao Fu & Yue Liu & Pengfei Cheng & Sijie Cheng, 2022. "Evolutionary Game Analysis on Innovation Behavior of Digital Financial Enterprises under the Dynamic Reward and Punishment Mechanism of Government," Sustainability, MDPI, vol. 14(19), pages 1-18, October.

    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. Marco Raberto & Silvano Cincotti & Sergio Focardi & Michele Marchesi, 2003. "Traders' Long-Run Wealth in an Artificial Financial Market," Computational Economics, Springer;Society for Computational Economics, vol. 22(2), pages 255-272, October.
    2. Mikhail Anufriev & Giulio Bottazzi, 2004. "Asset Pricing Model with Heterogeneous Investment Horizons," LEM Papers Series 2004/22, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Charles S. Tapiero, 2015. "A financial CCAPM and economic inequalities," Quantitative Finance, Taylor & Francis Journals, vol. 15(3), pages 521-534, March.
    4. Isao Yagi & Yuji Masuda & Takanobu Mizuta, 2020. "Analysis of the Impact of High-Frequency Trading on Artificial Market Liquidity," Papers 2010.13038, arXiv.org.
    5. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    6. Bottazzi, Giulio & Dosi, Giovanni & Rebesco, Igor, 2005. "Institutional architectures and behavioral ecologies in the dynamics of financial markets," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 197-228, February.
    7. Hommes, Cars & Huang, Hai & Wang, Duo, 2005. "A robust rational route to randomness in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 29(6), pages 1043-1072, June.
    8. Brock, William A. & Hommes, Cars H. & Wagener, Florian O. O., 2005. "Evolutionary dynamics in markets with many trader types," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 7-42, February.
    9. Scott C. Linn & Nicholas S. P. Tay, 2007. "Complexity and the Character of Stock Returns: Empirical Evidence and a Model of Asset Prices Based on Complex Investor Learning," Management Science, INFORMS, vol. 53(7), pages 1165-1180, July.
    10. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2001. "Microscopic Models of Financial Markets," Papers cond-mat/0110354, arXiv.org.
    11. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
    12. Mikhail Anufriev & Giulio Bottazzi, 2005. "Price and Wealth Dynamics in a Speculative Market with an Arbitrary Number of Generic Technical Traders," LEM Papers Series 2005/06, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    13. Baosheng Yuan & Kan Chen, 2005. "Impact of Investor's Varying Risk Aversion on the Dynamics of Asset Price Fluctuations," Papers physics/0506224, arXiv.org.
    14. Sornette, Didier & Zhou, Wei-Xing, 2006. "Importance of positive feedbacks and overconfidence in a self-fulfilling Ising model of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 704-726.
    15. Anufriev, Mikhail & Panchenko, Valentyn, 2009. "Asset prices, traders' behavior and market design," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1073-1090, May.
    16. J.-H. Steffi Yang & Satchell, S.E., 2002. "The Impact of Technical Analysis on Asset Price Dynamics," Cambridge Working Papers in Economics 0219, Faculty of Economics, University of Cambridge.
    17. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    18. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    19. Li, Y. & Donkers, A.C.D. & Melenberg, B., 2006. "The Non- and Semiparametric Analysis of MS Models : Some Applications," Other publications TiSEM c14adc9f-f490-40d6-81b7-8, Tilburg University, School of Economics and Management.
    20. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).

    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:jijfss:v:8:y:2020:i:1:p:2-:d:308832. 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.