Skip to main content
Log in

Credit rationing and the simulation of bank-small and medium sized firm artificial credit market

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

By analyzing the financing difficulties faced by the small and medium-sized firms, the paper built an artificial credit markets with the agent-based computational modeling to simulate the real world credit transactions. There are firms, banks, different risk-type projects as well as legal and supervision environments in which debt contracts constitute the financial instruments. The simulation results show that the number of collateral, average success probability of projects, and the prime interest rate have materially impact on bank’s average profit, bank’s capital, the overall interest rate, the number of borrowing firms, loan size, and the degree of credit rationing. These results in line with those of the classical SW model in the sense that the relationship between bank profits and interest rates is non-monotonic as well as the relationship between credit rationing and interest rates. And thus there is an adverse selection effect in credit rationing theory.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Myers S C and Majluf N S, Corporate financing and investment decisions when firms have information that investors do not have, Journal of Financial Economics, 1984, 13: 187–221.

    Article  Google Scholar 

  2. Wang X and Zhang J, On the bank credit rationing and loan of small and medium-sized enterprises (SMEs), Economic Research Journal, 2003, (7): 68–75.

    Google Scholar 

  3. Tian H P and Liu C X, Firm asset size, credit market structure and SME financing, Journal of Management Sciences in China, 2010, 5(13): 51–61.

    MathSciNet  Google Scholar 

  4. Berger A N, Saunders A, Scalise J M, et al., The effects of bank mergers and acquisitions on small business lending, Journal of Financial Economics, 1998, 50(2): 187–229.

    Article  Google Scholar 

  5. Lin Y F and Li Y J, Promoting the growth of medium and small-sized enterprises through the development of medium and small-sized financial institutions, Economic Research Journal, 2001, (1): 33–35.

    Google Scholar 

  6. Jaffee D M and Modigliani F, A theory and test of credit rationing, American Economic Review, 1969, 59(5): 851–872.

    Google Scholar 

  7. Stiglitz J E and Weiss A, Credit rationing in markets with imperfect information, American Economic Review, 1981, 71(3): 393–410.

    Google Scholar 

  8. Jaffee D M and Russell T, Imperfect information, uncertainty and credit rationing, Quarterly Journal of Economics, 1976, 90(4): 651–666.

    Article  Google Scholar 

  9. Manove M, Padilla A J, and Pagano M, Collateral versus project screening: A model of lazy banks, The RAND Journal of Economics, 2001, 32(4): 726–744.

    Article  Google Scholar 

  10. Hellmann T and Stiglitz J, Credit and equity rationing in markets with adverse selection, European Economic Review, 2000, 44(2): 281–304.

    Article  Google Scholar 

  11. Clementi F, Desiderio S, and Gallegatietal M, Computational experiments of policy design on goods, labour and credit markets, Working Paper, University of Bielefeld and University of Sheffield, Bielefeld & Sheffield, 2008.

    Google Scholar 

  12. Canzian G, Three essays in agent-based macroeconomics, Ph.D. Thesis, University of Trento, Trento, 2009.

    Google Scholar 

  13. Robertson D A, Agent-based models of a banking network as an example of a turbulent environment: The deliberate vs. emergent strategy debate revisited, Emergence, 2003, 5(2): 56–71.

    Article  Google Scholar 

  14. Barr J and Saraceno F, A computational theory of the firm, Journal of Economic Behavior & Organization, 2002, 49(3): 345–361.

    Article  Google Scholar 

  15. Cheng G, Zhang W, and Xiong X, Noisy information, structural model and bank evaluation of default probability, Journal of Management Sciences in China, 2007, 10(4): 38–48.

    Google Scholar 

  16. Xu F, Zhang W, Zhang Y, et al., Information identification in different networks with heterogeneous information sources, Journal of Systems Science and Complexity, 2014, 27(1): 92–116.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiong Xiong.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant Nos. 71532009, 71320107003 and 71271145, Core Projects in Tianjin Education Bureaus Social Science Program under Grant Nos. 2012JWZD11 and 2014ZD13, and Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20110032110031.

This paper was recommended for publication by Editor TANG Xijin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Zhang, W., Xiong, X. et al. Credit rationing and the simulation of bank-small and medium sized firm artificial credit market. J Syst Sci Complex 29, 991–1017 (2016). https://doi.org/10.1007/s11424-016-4007-x

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-016-4007-x

Keywords

Navigation