Predicting the Startup Valuation: A deep learning approach

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 th...

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Bibliographic Details
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
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Summary: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.
ISSN:1369-1066
1464-5343
DOI:10.1080/13691066.2022.2161968