Modeling Venture Capital Exit Time Using a Parametric Accelerated Failure Time Model

Document Type : Research Paper

Authors

1 MSc., Department of Financial Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.

2 Assistant Prof., Department of Financial Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.

10.22059/frj.2025.366656.1007524

Abstract

Objective
Venture capitalists (VCs) are not long-term investors and generally exit a startup with a specific strategy, once their anticipated returns are secured. The timing and strategy of venture capital (VC) exit are critical factors that determine investors’ returns and significantly influence the success or failure of the new venture. Given the nascent nature of venture capital and the limited exit experience in Iran, the decision regarding the optimal exit time is a significant concern for Iranian VCs. Therefore, recognizing the importance of this decision, the present study aims to model the VC exit time by considering the factors that VCs use to determine their exit strategy from a startup.
 
Methods
To identify the factors influencing the timing and strategy of exit decisions, semi‑structured interviews were conducted with experts in the Iranian venture capital sector using convenience sampling. Subsequently, to assess the impact of these factors on the exit time of Iranian VCs, data were collected based on the identified variables. A questionnaire, developed based on existing venture capital studies, was distributed among all members of the statistical sample. The collected data were analyzed using the Competing Risks Survival Analysis method to simultaneously model the exit time and strategy.
 
Results
The interviews identified 24 factors influencing the exit decision time and 7 exit strategies VCs had employed. In the subsequent modeling, all 24 factors were initially included as covariates. To eliminate factors with minimal impact, correlation and univariate analyses reduced the set to 16 factors, upon which a multivariate model was built. For multivariate modeling, Accelerated Failure Time (AFT) regression models were used. The results indicate that the AFT model based on the Weibull distribution is suitable for the survival data. The impact of each factor on the exit time was then assessed using this model.
 
Conclusion
The results suggest that, across the exit strategies of Management Buyout (MBO), divestiture to a private investor, merger and acquisition (M&A), divestiture to stakeholders, and liquidation, the variable of Return on Investment (ROI)—a key factor highlighted in prior research—leads to a decrease in the exit hazard rate and consequently an increase in the investment duration. Furthermore, the variable “existence of a positive outlook for the startup’s future,” identified as a critical factor by Iranian VCs, reduces the exit hazard rate. This, in turn, prolongs the investment duration for strategies such as management buyout (MBO), divestiture to partners, divestiture to another VC, and divestiture to stakeholders.

Keywords

Main Subjects

References

 
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Financial Research Journal
Volume 28, Issue 2
2026
Pages 328-348

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