Predicting the Startup Valuation: A deep learning approach

Monika Dhochak, Sudesh Pahal and Prince Doliya

Venture Capital, 2024, vol. 26, issue 1, 75-99

Abstract: The investment and funding decisions of a new venture are based on the startup valuation, which remains an inconclusive and disputable subject matter. For this purpose, well-established strategic management theories such as resource-based view (RBV), industrial structure effect, and network-based theory have been leveraged as inputs. This study uses 757 Indian startup deals dataset during the period from January 2012 to December 2019 to develop a predictive model based on the Artificial Neural Network (ANN) technique, which is a deep learning approach to predict the startup valuation. The ANN-based model predicts the startup pre-money valuation, and we also compares the ANN model to a linear classifier, linear regression, in this study. The result shows that the application of the ANN model can be used as a supplementary method to predict the pre-money valuation, if not an alternative to the traditional valuation models depending on its adaptability and accuracy. This model provides a competitive advantage by building a strong foundation during the negotiation between VCs and entrepreneurs. This study provides managerial and theoretical implications to VCs, entrepreneurs, and policy-makers for upgrading the startup ecosystem.

Date: 2024
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DOI: 10.1080/13691066.2022.2161968

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