Skip to main content

Advertisement

Springer Nature Link
Log in

Farmers credit optimization decision model and applications based on common risk guarantee fund

  • S.I. : SPIoT 2020
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The paper proposes the farmers credit optimization decision model and applications based on common risk guarantee fund. By considering two conditions of with default risk and without default risk, the individual rationality of bank and farmer is designed, respectively, and the mathematical formulas for calculating the risk loss ratio of bank loans under the guarantee fund are established and discussed innovatively, respectively. A nonlinear optimal model based on risk compensation fund is established in the paper. By mathematical proof, the optimal bank credit decision mechanism is studied. By the analysis of numeric experiments of two cases of with default risk and without default risk, the expected income and invest income are varied by the changing of farmer project success probability and default probability, and the relationship of loan interest, farmer expected income and invest income is studied. The research has scientific guiding significance and practical application value for farmers' credit decision making.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Fujita K (2000) Credit flowing from the poor to the rich: the financial market and the role of the Grameen Bank in Rural Bangladesh. Develop Econ 38(3):343–373

    Article  Google Scholar 

  2. Chaudhary M, Ishfaq M (2003) Credit worthiness of rural borrowers of Pakistan[J]. J Socio-Econ 32:675–684

    Article  Google Scholar 

  3. Pal S (2002) Household sectoral choice and effective demand for rural credit in India[J]. Appl Econ 9(34):1743–1755

    Article  Google Scholar 

  4. Jaffee D, Franco M (1969) A theory and test of credit rationing. J Am Econ Rev 59:850–872

    Google Scholar 

  5. Jaffee D, Russell T (1976) The imperfect information, uncertainty and credit rationing[J]. Quart J Econ 90(4):651–666

    Article  Google Scholar 

  6. Stiglitz J, Weiss A (1981) Credit rationing in markets with imperfect information[J]. Am Econ Rev 71(3):393–410

    Google Scholar 

  7. Bester H (1985) Screening vs rationing in credit markets with imperfect information. J Am Econ Rev 75(4):850–855

    Google Scholar 

  8. Besanko D, Thakor AV (1987) Collateral and rationing: Sorting equilibtia in monopolistic and competitive credit markets[J]. Int J Econ Rev 28(3):671–689

    Article  Google Scholar 

  9. Jing W, Wang H, Dong X (1996) On banks’ credit decision mechanism (1) - the case of credit market with perfect competition. J Syst Eng 11(2):52–58

    Google Scholar 

  10. Jing W, Wang H, Dong X (1996) On banks’ credit decision mechanism (2) - the case of credit market with perfect competition. J Syst Eng 11(2):59–65

    Google Scholar 

  11. Pang S, Li R, Liu Y, Guo X (1999) The decision mechanism of bank’s credit risk with imperfect information. J South China Univ Technol 27(8):20–25 ((in Chinese))

    Google Scholar 

  12. Pang S (2007) Credit risk decision model under the effects of the default probability[J]. Syst Eng - Theor Pract 27(10):31–39

    Google Scholar 

  13. Pang S, Wang Y (2008) Credit decision model and mechanism with default risk parameter. J Syst Eng - Theor Pract 28(8):81–88

    Article  Google Scholar 

  14. Pang S (2012) Credit decision model and mechanisms under default risk. J Sci Manag 4:242–246 ((in Chinese))

    Google Scholar 

  15. Jensen FE (2000) The farm credit system as a government-sponsored enterprise. J Rev Agric Econ 22(2):326–335

    Article  Google Scholar 

  16. Honohan P (2010) Partial credit guarantees: principles and practice[J]. J Financ Stab 6(1):1–9

    Article  Google Scholar 

  17. Green RK (2011) Housing finance reform: should there be a government guarantee?[J]. Q J Financ 1(04):811–822

    Article  Google Scholar 

  18. Lapachi K, Ormotsadze R (2012) Credit guarantee fund for Georgia[J]. J Bus 1(1):5–12

    Google Scholar 

  19. Devinaga R, Tan TM (2012) Review of credit guarantee corporation malaysia (cgcm) initiatives to enhance small and medium enterprises performance[J]. Int J Business Manag 7(20):101

    Article  Google Scholar 

  20. Turvey CG, Wang Y (2012) The effects of government sponsored enterprise (GSE) status on the pricing of bonds issued by the federal farm credit banks funding corporation (FFCB)[J]. Agric Financ Rev 72(3):488–506

    Article  Google Scholar 

  21. Beck T, Klapper LF, Mendoza JC (2010) The typology of partial credit guarantee funds around the world[J]. J Financ Stab 6(1):10–25

    Article  Google Scholar 

  22. F Columba, L Gambacorta, PE Mistrulli (2009) The effects of mutual guarantee consortia on the quality of bank lending[J]

  23. Columba F, Gambacorta L, Mistrulli PE (2010) Mutual guarantee institutions and small business finance[J]. J Financ Stab 6(1):45–54

    Article  Google Scholar 

  24. Busetta G, Zazzaro A (2012) Mutual loan-guarantee societies in monopolistic credit markets with adverse selection[J]. J Financ Stab 8(1):15–24

    Article  Google Scholar 

  25. Pang S (2012) Farmer’s credit decision model based on risk guarantee fund. Fifth Int Conf Business Int Financ Eng 8:242–246

    Google Scholar 

  26. Nemoto T, Ogura Y, Watanabe W (2013) The decision-making mechanism of regional financial institutions and the utilization of soft information[J]. Policy Res Inst, Ministry of Financ, Japan, Pub Policy Rev 9(1):87–115

    Google Scholar 

  27. Borges VA, Lima FG, Júnior TP, Gaio LE (2014) Impact of the segmentation for models of analysis in granting: a comparison of results. Business Manag Rev 3(10):1–16

    Google Scholar 

  28. Waemustafa W, Sukri S (2015) Bank specific and macroeconomics dynamic determinants of credit risk in islamic banks and conventional banks. Int J Econ Financ Issues 5:476–481

    Google Scholar 

  29. Morris S, Shin HS (2016) Illiquidity component of credit risk. Int Econ Rev 57(4):1135–1148

    Article  Google Scholar 

Download references

Acknowledgements

The paper is supported by National Natural Science Foundation of China (91646112, 71173089)

Author information

Authors and Affiliations

  1. School of Emergency Management/Institute of Finance Engineering, Jinan University, Guangzhou, 510632, China

    Sulin Pang

  2. School of Business, Macau University of Science and Technology, Avenida WaiLong, Taipa, Macau, 999078, China

    Sulin Pang & Min Wei

  3. School of Emergency Industry, Guangzhou Pearl-River College of Vocational Technology, Huizhou, 516131, Guangdong, China

    Sulin Pang, Min Wei & Zhiming Wen

  4. Guangdong Emergency Technology Research Center of Risk Evaluation and Prewarning On Public Network Security, Guangzhou, 510632, Guangdong, China

    Sulin Pang & Min Wei

Authors
  1. Sulin Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiming Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sulin Pang.

Ethics declarations

Conflict of interest

This paper is an original research and has not been submitted to any other journals. So there is no any conflict of interest in any other journals.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pang, S., Wei, M. & Wen, Z. Farmers credit optimization decision model and applications based on common risk guarantee fund. Neural Comput & Applic 33, 4053–4064 (2021). https://doi.org/10.1007/s00521-020-05651-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05651-7

Keywords