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A New Multi-Dimensional Framework for Start-Ups Lifespan Assessment Using Bayesian Networks

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  • Mohammadreza Valaei

    (Industrial Engineering Department, Bu-Ali Sina University, Hamedan 6516738695, Iran)

  • Vahid Khodakarami

    (Industrial Engineering Department, Bu-Ali Sina University, Hamedan 6516738695, Iran)

Abstract

As historical data are typically unavailable for a start-up, risk assessment is always complex and challenging. Traditional methods are incapable of capturing all facets of this complexity; therefore, more sophisticated tools are necessary. Using an expert-elicited Bayesian networks (BNs) methodology, this paper aims to provide a method for combining diverse sources of information, such as historical data, expert knowledge, and the unique characteristics of each start-up, to estimate the default rate at various stages of the life cycle. The proposed method not only reduces the cognitive error of expert opinion for a new start-up but also considers the learning feature of BNs and the effect of lifespan when updating default estimations. In addition, the model considers the impact of investors’ risk appetite. Furthermore, the model can rank the most effective risk factors at various stages. The receiver operating characteristic (ROC) curve was utilized to assess the model’s explanatory power. Moreover, three distinct case studies were used to demonstrate the model’s capabilities.

Suggested Citation

  • Mohammadreza Valaei & Vahid Khodakarami, 2023. "A New Multi-Dimensional Framework for Start-Ups Lifespan Assessment Using Bayesian Networks," JRFM, MDPI, vol. 16(2), pages 1-19, February.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:2:p:88-:d:1054577
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    References listed on IDEAS

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    1. Nadkarni, Sucheta & Shenoy, Prakash P., 2001. "A Bayesian network approach to making inferences in causal maps," European Journal of Operational Research, Elsevier, vol. 128(3), pages 479-498, February.
    2. Stjepan Srhoj & Bruno Škrinjarić & Sonja Radas, 2021. "Bidding against the odds? The impact evaluation of grants for young micro and small firms during the recession," Small Business Economics, Springer, vol. 56(1), pages 83-103, January.
    3. Edward I. Altman & Marco Balzano & Alessandro Giannozzi & Stjepan Srhoj, 2023. "Revisiting SME default predictors: The Omega Score," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(6), pages 2383-2417, November.
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