giacomobellodi01/Modeling-Underwriting-Syndicates-to-Predict-IPO-Performance-

This project is a Master's thesis (MSc) implementation, scoring a 2 on defensibility due to its status as a personal academic experiment with no current stars, forks, or community traction. While the methodology—using Hypergraph Neural Networks (HGNNs) to model multi-party underwriting syndicates—is a sophisticated 'novel combination' of graph theory and finance, it lacks a technical moat. The dataset (3,109 IPOs) is relatively small for modern deep learning, and the implementation serves as a reference for academic findings rather than a production-ready tool. Frontier labs (OpenAI, Google) are unlikely to compete here as the domain is too niche and specialized. However, the risk of displacement is high and the horizon is short (6 months) because the core value lies in the feature engineering approach, which any quantitative hedge fund or financial data provider (e.g., Bloomberg, Refinitiv) could replicate or exceed using their proprietary datasets. The 'moat' would require a continuous data pipeline and integration into trading execution systems, which this repo does not provide.