Graph Neural Network Based VC Investment Success Prediction

TL;DR

This paper introduces a novel graph neural network approach that leverages network structures and node attributes to predict start-up success in venture capital investments, outperforming human investors and providing insights into success factors.

Contribution

It presents an incremental representation learning and sequential model that captures complex stakeholder networks, achieving state-of-the-art prediction accuracy and revealing key success factors.

Findings

Outperforms human investors in prediction accuracy.

Excels in healthcare and IT industry start-up predictions.

Identifies gender, education, and networking as influential success factors.

Abstract

Predicting the start-ups that will eventually succeed is essentially important for the venture capital business and worldwide policy makers, especially at an early stage such that rewards can possibly be exponential. Though various empirical studies and data-driven modeling work have been done, the predictive power of the complex networks of stakeholders including venture capital investors, start-ups, and start-ups' managing members has not been thoroughly explored. We design an incremental representation learning mechanism and a sequential learning model, utilizing the network structure together with the rich attributes of the nodes. In general, our method achieves the state-of-the-art prediction performance on a comprehensive dataset of global venture capital investments and surpasses human investors by large margins. Specifically, it excels at predicting the outcomes for start-ups in industries such as healthcare and IT. Meanwhile, we shed light on impacts on start-up success from observable factors including gender, education, and networking, which can be of value for practitioners as well as policy makers when they screen ventures of high growth potentials.