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Predicting startup success presents a formidable challenge due to theinherently volatile landscape of the entrepreneurial ecosystem. The advent ofextensive databases like Crunchbase jointly with available open data enablesthe application of machine learning and artificial intelligence for moreaccurate predictive analytics. This paper focuses on startups at their Series Band Series C investment stages, aiming to predict key success milestones suchas achieving an Initial Public Offering (IPO), attaining unicorn status, orexecuting a successful Merger and Acquisition (M\&A). We introduce novel deeplearning model for predicting startup success, integrating a variety of factorssuch as funding metrics, founder features, industry category. A distinctivefeature of our research is the use of a comprehensive backtesting algorithmdesigned to simulate the venture capital investment process. This simulationallows for a robust evaluation of our model's performance against historicaldata, providing actionable insights into its practical utility in real-worldinvestment contexts. Evaluating our model on Crunchbase's, we achieved a 14times capital growth and successfully identified on B round high-potentialstartups including Revolut, DigitalOcean, Klarna, Github and others. Ourempirical findings illuminate the importance of incorporating diverse featuresets in enhancing the model's predictive accuracy. In summary, our workdemonstrates the considerable promise of deep learning models and alternativeunstructured data in predicting startup success and sets the stage for futureadvancements in this research area.

Startup success prediction and VC portfolio simulation using CrunchBase data