Startup success prediction and VC portfolio simulation using CrunchBase
  data
Mark Potanin Andrey Chertok Konstantin Zorin Cyril Shtabtsovsky
Predicting startup success presents a formidable challenge due to the
inherently volatile landscape of the entrepreneurial ecosystem. The advent of
extensive databases like Crunchbase jointly with available open data enables
the application of machine learning and artificial intelligence for more
accurate predictive analytics. This paper focuses on startups at their Series B
and Series C investment stages, aiming to predict key success milestones such
as achieving an Initial Public Offering (IPO), attaining unicorn status, or
executing a successful Merger and Acquisition (M\&A). We introduce novel deep
learning model for predicting startup success, integrating a variety of factors
such as funding metrics, founder features, industry category. A distinctive
feature of our research is the use of a comprehensive backtesting algorithm
designed to simulate the venture capital investment process. This simulation
allows for a robust evaluation of our model's performance against historical
data, providing actionable insights into its practical utility in real-world
investment contexts. Evaluating our model on Crunchbase's, we achieved a 14
times capital growth and successfully identified on B round high-potential
startups including Revolut, DigitalOcean, Klarna, Github and others. Our
empirical findings illuminate the importance of incorporating diverse feature
sets in enhancing the model's predictive accuracy. In summary, our work
demonstrates the considerable promise of deep learning models and alternative
unstructured data in predicting startup success and sets the stage for future
advancements in this research area.