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Firms in financial distress: evidence from inter-firm payment networks with volatility driven by ‘animal spirits’

Author

Listed:
  • Rémi Stellian

    (Pontificia Universidad Javeriana)

  • Gabriel I. Penagos

    (Pontificia Universidad Javeriana)

  • Jenny P. Danna-Buitrago

    (Los Libertadores University Institute)

Abstract

This paper elaborates an agent-based model of a pure market economy to provide theoretical evidence on how volatility-induced changes in inter-firm payment networks affect the financial distress of firms. This volatility is driven by ‘animal spirits’ in that it arises from the feelings of optimism/pessimism independently of rational decision-making, and influences the liquidity available to each firm through the inter-firm payment network; consequently, some firms may enter financial distress. The model first determines the inter-firm payment network. Then, a mean-reverting square-root process introduces volatility into the inter-firm payment network through firms’ propensity to pay suppliers according to the payments that firms expect to receive from customers. The model is calibrated for compatibility with relevant macro- and microeconomic stylized facts. According to computational experiments, financial distress in the business sector is minimized when feelings of optimism/pessimism generate the lowest volatility in firms’ propensity to pay suppliers. In addition, this volatility must materialize around an intermediate value of firms’ propensity to pay suppliers, and firms must keep this intermediate value over time.

Suggested Citation

  • Rémi Stellian & Gabriel I. Penagos & Jenny P. Danna-Buitrago, 2021. "Firms in financial distress: evidence from inter-firm payment networks with volatility driven by ‘animal spirits’," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(1), pages 59-101, January.
    • Handle: RePEc:spr:jeicoo:v:16:y:2021:i:1:d:10.1007_s11403-020-00285-3
    DOI: 10.1007/s11403-020-00285-3
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    1. Bruneau, C. & de Bandt, O. & El Amri, W., 2012. "Macroeconomic fluctuations and corporate financial fragility," Journal of Financial Stability, Elsevier, vol. 8(4), pages 219-235.
    2. Recchioni, M.C. & Sun, Y., 2016. "An explicitly solvable Heston model with stochastic interest rate," European Journal of Operational Research, Elsevier, vol. 249(1), pages 359-377.
    3. Riccetti, Luca & Russo, Alberto & Gallegati, Mauro, 2016. "Stock market dynamics, leveraged network-based financial accelerator and monetary policy," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 509-524.
    4. Bagliano, Fabio C. & Morana, Claudio, 2014. "Determinants of US financial fragility conditions," Research in International Business and Finance, Elsevier, vol. 30(C), pages 377-392.
    5. Hernandez Tinoco, Mario & Wilson, Nick, 2013. "Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 394-419.
    6. Battiston, Stefano & Delli Gatti, Domenico & Gallegati, Mauro & Greenwald, Bruce & Stiglitz, Joseph E., 2012. "Liaisons dangereuses: Increasing connectivity, risk sharing, and systemic risk," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1121-1141.
    7. Erlingsson, Einar Jon & Teglio, Andrea & Cincotti, Silvano & Stefansson, Hlynur & Sturlusson, Jon Thor & Raberto, Marco, 2014. "Housing market bubbles and business cycles in an agent-based credit economy," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 8, pages 1-42.
    8. Lengnick, Matthias, 2013. "Agent-based macroeconomics: A baseline model," Journal of Economic Behavior & Organization, Elsevier, vol. 86(C), pages 102-120.
    9. Riccetti, Luca & Russo, Alberto & Gallegati, Mauro, 2013. "Leveraged network-based financial accelerator," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1626-1640.
    10. Dhami, Sanjit, 2016. "The Foundations of Behavioral Economic Analysis," OUP Catalogue, Oxford University Press, number 9780198715535, Decembrie.
    11. Caiani, Alessandro & Godin, Antoine & Caverzasi, Eugenio & Gallegati, Mauro & Kinsella, Stephen & Stiglitz, Joseph E., 2016. "Agent based-stock flow consistent macroeconomics: Towards a benchmark model," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 375-408.
    12. Yun, Youngyun, 2018. "The moments of a diffusion process," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 36-41.
    13. Assenza, Tiziana & Delli Gatti, Domenico & Grazzini, Jakob, 2015. "Emergent dynamics of a macroeconomic agent based model with capital and credit," Journal of Economic Dynamics and Control, Elsevier, vol. 50(C), pages 5-28.
    14. Webb, Craig S. & Zank, Horst, 2011. "Accounting for optimism and pessimism in expected utility," Journal of Mathematical Economics, Elsevier, vol. 47(6), pages 706-717.
    15. Jean Cartelier, 2018. "Money, Markets, Capital: The Case for a Monetary Analysis," Post-Print hal-01701095, HAL.
    16. El Kalak, Izidin & Hudson, Robert, 2016. "The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model," International Review of Financial Analysis, Elsevier, vol. 43(C), pages 135-145.
    17. Fallahpour, Saeid & Lakvan, Eisa Norouzian & Zadeh, Mohammad Hendijani, 2017. "Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem," Journal of Retailing and Consumer Services, Elsevier, vol. 34(C), pages 159-167.
    18. Pech, Wesley & Milan, Marcelo, 2009. "Behavioral economics and the economics of Keynes," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 38(6), pages 891-902, December.
    19. Jin Zhang & David A. Bessler & David J. Leatham, 2013. "Aggregate Business Failures and Macroeconomic Conditions: A Var Look at the U.S. Between 1980 and 2004," Journal of Applied Economics, Taylor & Francis Journals, vol. 16(1), pages 179-202, May.
    20. Weinstock, Eyal & Sonsino, Doron, 2014. "Are risk-seekers more optimistic? Non-parametric approach," Journal of Economic Behavior & Organization, Elsevier, vol. 108(C), pages 236-251.
    21. Hyman P. Minsky, 1992. "The Financial Instability Hypothesis," Economics Working Paper Archive wp_74, Levy Economics Institute.
    22. Li, Shouwei & Sui, Xin, 2016. "Contagion risk in endogenous financial networks," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 591-597.
    23. George A. Akerlof & Robert J. Shiller, 2010. "Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism," Economics Books, Princeton University Press, edition 1, number 9163.
    24. De Socio, Antonio & Michelangeli, Valentina, 2017. "A model to assess the financial vulnerability of Italian firms," Journal of Policy Modeling, Elsevier, vol. 39(1), pages 147-168.
    25. Faruk Ülgen, Ed. & Matthieu Méaulle & Rémi Stellian & Ramón Tortajada, 2013. "New contributions to monetary analysis : the foundations of an alternative economic paradigm," Post-Print halshs-00847198, HAL.
    26. Battiston, Stefano & Delli Gatti, Domenico & Gallegati, Mauro & Greenwald, Bruce & Stiglitz, Joseph E., 2007. "Credit chains and bankruptcy propagation in production networks," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 2061-2084, June.
    27. Joaquin, Domingo Castelo, 2016. "On animal spirits and economic decisions: Value-at-Risk and Value-within-Reach as measures of risk and return," The Quarterly Review of Economics and Finance, Elsevier, vol. 60(C), pages 231-233.
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    More about this item

    Keywords

    Financial distress; Inter-firm payment network; Animal spirits; Volatility; Agent-based model; Mean-reverting square-root process;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E40 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - General
    • G31 - Financial Economics - - Corporate Finance and Governance - - - Capital Budgeting; Fixed Investment and Inventory Studies

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