Predicting the Startup Valuation: A deep learning approach

• Published in: Venture capital (London) 2024-01, Vol.26 (1), p.75-99
• Main Authors: Dhochak, Monika, Pahal, Sudesh, Doliya, Prince
• Format: Article
• Language: English
• Subjects: ANN-based model; Competitive advantage; Deep learning; Entrepreneurs; Industrial structure; Learning; Money; neural network; Neural networks; Policy making; Prediction models; Startup valuation; Startups; Strategic management; Valuation; venture capitalists
• ISSN: 1369-1066; 1464-5343
• DOI: 10.1080/13691066.2022.2161968

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.