IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v257y2017i3p1045-1054.html
   My bibliography  Save this article

Behaviour-based short-term invoice probability of default evaluation

Author

Listed:
  • Perko, Igor

Abstract

In this paper, the effect of behavioural analytics on short-term default predictions at the invoice level is addressed by answering a question that slightly diverges from the traditional probability of default definition: ‘What is the probability that this invoice will be paid within the next 30 days?’ Resultantly improving short-term liquidity planning accuracy and supporting financial management in companies.

Suggested Citation

  • Perko, Igor, 2017. "Behaviour-based short-term invoice probability of default evaluation," European Journal of Operational Research, Elsevier, vol. 257(3), pages 1045-1054.
    • Handle: RePEc:eee:ejores:v:257:y:2017:i:3:p:1045-1054
    DOI: 10.1016/j.ejor.2016.08.039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221716306786
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2016.08.039?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pagano, Marco & Jappelli, Tullio, 1993. "Information Sharing in Credit Markets," Journal of Finance, American Finance Association, vol. 48(5), pages 1693-1718, December.
    2. John S. Earle & Klara Z. Sabirianova, 2002. "How Late to Pay? Understanding Wage Arrears in Russia," Journal of Labor Economics, University of Chicago Press, vol. 20(3), pages 661-707, July.
    3. Premachandra, I.M. & Bhabra, Gurmeet Singh & Sueyoshi, Toshiyuki, 2009. "DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique," European Journal of Operational Research, Elsevier, vol. 193(2), pages 412-424, March.
    4. Teemu Kautonen & Marco van Gelderen & Erno T. Tornikoski, 2013. "Predicting entrepreneurial behaviour: a test of the theory of planned behaviour," Applied Economics, Taylor & Francis Journals, vol. 45(6), pages 697-707, February.
    5. Barro, Diana & Basso, Antonella, 2010. "Credit contagion in a network of firms with spatial interaction," European Journal of Operational Research, Elsevier, vol. 205(2), pages 459-468, September.
    6. Doumpos, M. & Kosmidou, K. & Baourakis, G. & Zopounidis, C., 2002. "Credit risk assessment using a multicriteria hierarchical discrimination approach: A comparative analysis," European Journal of Operational Research, Elsevier, vol. 138(2), pages 392-412, April.
    7. Dionne, Georges & Laajimi, Sadok, 2012. "On the determinants of the implied default barrier," Journal of Empirical Finance, Elsevier, vol. 19(3), pages 395-408.
    8. Bing Zhu & Changzheng He & Xiaoyi Jiang, 2015. "Customer choice prediction based on transfer learning," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(6), pages 1044-1051, June.
    9. Li, Ming-Yuan Leon & Miu, Peter, 2010. "A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: A binary quantile regression approach," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 818-833, September.
    10. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2015. "Support vector regression for loss given default modelling," European Journal of Operational Research, Elsevier, vol. 240(2), pages 528-538.
    11. Sergei A. Davydenko & Ilya A. Strebulaev, 2007. "Strategic Actions and Credit Spreads: An Empirical Investigation," Journal of Finance, American Finance Association, vol. 62(6), pages 2633-2671, December.
    12. Kim, Hong Sik & Sohn, So Young, 2010. "Support vector machines for default prediction of SMEs based on technology credit," European Journal of Operational Research, Elsevier, vol. 201(3), pages 838-846, March.
    13. Wang, Ashley W. & Zhang, Gaiyan, 2009. "Institutional ownership and credit spreads: An information asymmetry perspective," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 597-612, September.
    14. Hu, Yu-Chiang & Ansell, Jake, 2007. "Measuring retail company performance using credit scoring techniques," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1595-1606, December.
    15. Kosmidou K. & Doumpos M. & Zopounidis C., 2002. "A Multicriteria Hierarchical Discrimination Approach for Credit Risk Problems," European Research Studies Journal, European Research Studies Journal, vol. 0(1-2), pages 53-68, January -.
    16. Leow, Mindy & Crook, Jonathan, 2014. "Intensity models and transition probabilities for credit card loan delinquencies," European Journal of Operational Research, Elsevier, vol. 236(2), pages 685-694.
    17. Davidson, Ian & Tayi, Giri, 2009. "Data preparation using data quality matrices for classification mining," European Journal of Operational Research, Elsevier, vol. 197(2), pages 764-772, September.
    18. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn C., 2012. "Mixture cure models in credit scoring: If and when borrowers default," European Journal of Operational Research, Elsevier, vol. 218(1), pages 132-139.
    19. Finlay, Steven, 2010. "Credit scoring for profitability objectives," European Journal of Operational Research, Elsevier, vol. 202(2), pages 528-537, April.
    20. R Florez-Lopez, 2010. "Effects of missing data in credit risk scoring. A comparative analysis of methods to achieve robustness in the absence of sufficient data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 486-501, March.
    21. Allen, Franklin, 1981. "The Prevention of Default," Journal of Finance, American Finance Association, vol. 36(2), pages 271-276, May.
    22. Michael Wood & Theodore Noseworthy & Scott Colwell, 2013. "If You Can’t See the Forest for the Trees, You Might Just Cut Down the Forest: The Perils of Forced Choice on “Seemingly” Unethical Decision-Making," Journal of Business Ethics, Springer, vol. 118(3), pages 515-527, December.
    23. Blöchlinger, Andreas, 2012. "Validation of Default Probabilities," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 47(5), pages 1089-1123, October.
    24. Jappelli, Tullio & Pagano, Marco, 2002. "Information sharing, lending and defaults: Cross-country evidence," Journal of Banking & Finance, Elsevier, vol. 26(10), pages 2017-2045, October.
    25. Altman, Edward I. & Rijken, Herbert A., 2004. "How rating agencies achieve rating stability," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2679-2714, November.
    26. Li Chen & A. Gürhan Kök & Jordan D. Tong, 2013. "The Effect of Payment Schemes on Inventory Decisions: The Role of Mental Accounting," Management Science, INFORMS, vol. 59(2), pages 436-451, September.
    27. Delia Coculescu & Hélyette Geman & Monique Jeanblanc, 2008. "Valuation of default-sensitive claims under imperfect information," Finance and Stochastics, Springer, vol. 12(2), pages 195-218, April.
    28. Ju, Yonghan & Jeon, Song Yi & Sohn, So Young, 2015. "Behavioral technology credit scoring model with time-dependent covariates for stress test," European Journal of Operational Research, Elsevier, vol. 242(3), pages 910-919.
    29. Orth, Walter, 2013. "Multi-period credit default prediction with time-varying covariates," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 214-222.
    30. Crouhy, Michel & Galai, Dan & Mark, Robert, 2000. "A comparative analysis of current credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 59-117, January.
    31. Wolter, Marcus & Rösch, Daniel, 2014. "Cure events in default prediction," European Journal of Operational Research, Elsevier, vol. 238(3), pages 846-857.
    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. Wanying Song & Jian Min & Jianbo Yang, 2023. "Credit Risk Assessment of Heavy-Polluting Enterprises: A Wide- ℓ p Penalty and Deep Learning Approach," Mathematics, MDPI, vol. 11(16), pages 1-19, August.

    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. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    2. Şaban Çelik & Bora Aktan & Bruce Burton, 2022. "Firm dynamics and bankruptcy processes: A new theoretical model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 567-591, April.
    3. Ioannis Tsolas, 2015. "Firm credit risk evaluation: a series two-stage DEA modeling framework," Annals of Operations Research, Springer, vol. 233(1), pages 483-500, October.
    4. Liu, Jia & Riyanto, Yohanes E., 2019. "Liquidation policy and credit history in financial contracting: An experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 158(C), pages 526-542.
    5. Djimoudjiel, Djekonbe & T. Rostand, Dany Dombu & MBATINA NODJI, NDILENGAR, 2024. "What lessons does the COVID-19 pandemic teach us about banking liquidity and information share in the CEMAC zone?," MPRA Paper 119666, University Library of Munich, Germany, revised 17 Jan 2024.
    6. Tsionas, Mike G., 2019. "Multi-objective optimization using statistical models," European Journal of Operational Research, Elsevier, vol. 276(1), pages 364-378.
    7. Christa Hainz, 2011. "Measuring Information Sharing in Credit Markets," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 9(1), pages 21-27, 05.
    8. Love, Inessa & Peria, Maria Soledad Martinez & Singh, Sandeep, 2013. "Collateral registries for movable assets : does their introduction spur firms'access to bank finance ?," Policy Research Working Paper Series 6477, The World Bank.
    9. Dana C. Andersen, 2016. "Credit Constraints, Technology Upgrading, and the Environment," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 3(2), pages 283-319.
    10. Brei, Michael & Jacolin, Luc & Noah, Alphonse, 2020. "Credit risk and bank competition in Sub-Saharan Africa," Emerging Markets Review, Elsevier, vol. 44(C).
    11. Djankov, Simeon & McLiesh, Caralee & Shleifer, Andrei, 2007. "Private credit in 129 countries," Journal of Financial Economics, Elsevier, vol. 84(2), pages 299-329, May.
    12. Shusen Qi & Ralph De Haas & Steven Ongena & Stefan Straetmans & Tamas Vadasz, 2017. "Move a Little Closer? Information Sharing and the Spatial Clustering of Bank Branches," Swiss Finance Institute Research Paper Series 17-74, Swiss Finance Institute, revised Jun 2023.
    13. Hainz, Christa, 2009. "Creditor passivity: The effects of bank competition and institutions on the strategic use of bankruptcy filings," Journal of Comparative Economics, Elsevier, vol. 37(4), pages 582-596, December.
    14. Gu, Xian & Kowalewski, Oskar, 2016. "Creditor rights and the corporate bond market," Journal of International Money and Finance, Elsevier, vol. 67(C), pages 215-238.
    15. Simplice A. Asongu & John C. Anyanwu & Vanessa S. Tchamyou, 2019. "Technology-driven information sharing and conditional financial development in Africa," Information Technology for Development, Taylor & Francis Journals, vol. 25(4), pages 630-659, October.
    16. Behr, Patrick & Sonnekalb, Simon, 2012. "The effect of information sharing between lenders on access to credit, cost of credit, and loan performance – Evidence from a credit registry introduction," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 3017-3032.
    17. Maclachlan, Iain C, 2007. "An empirical study of corporate bond pricing with unobserved capital structure dynamics," MPRA Paper 28416, University Library of Munich, Germany.
    18. Guérineau, Samuel & Léon, Florian, 2019. "Information sharing, credit booms and financial stability: Do developing economies differ from advanced countries?," Journal of Financial Stability, Elsevier, vol. 40(C), pages 64-76.
    19. Asongu, Simplice & Anyanwu, John & Tchamyou, Vanessa, 2016. "Information sharing and conditional financial development in Africa," MPRA Paper 74653, University Library of Munich, Germany.
    20. Mariassunta Giannetti & José María Liberti & Jason Sturgess, 2017. "Information Sharing and Rating Manipulation," Review of Financial Studies, Society for Financial Studies, vol. 30(9), pages 3269-3304.

    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:eee:ejores:v:257:y:2017:i:3:p:1045-1054. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.