The Geoeconomics of Venture Capital: An Economic Complexity Approach to Emerging Technological Sovereignty

Battle Count: 50

The paper is primarily focused on geoeconomic policy analysis and technological sovereignty assessment rather than financial markets or trading strategies. However, the economic complexity framework and country-technology specialization metrics could inform thematic investment strategies, sector rotation based on national technology positioning, or geopolitical risk models. The GCI/ETGCI rankings could serve as macro-level signals for identifying countries with emerging technology advantages, potentially relevant for cross-border venture capital fund allocation or technology-sector ETF positioning. The paper does not address asset pricing, return prediction, or trading signals directly.

The core methodology involves: (1) data collection from Crunchbase (commercial API), (2) LLM-based multi-label classification of startups into 18 domains with probability calibration, (3) construction of RVA-based binary specialization matrix, (4) eigenvalue decomposition of country-country and technology-technology matrices, (5) relatedness-based simulation of single-addition specializations. The mathematical framework (eigenvalue problems, bipartite network analysis) is well-established in economic complexity literature. The main complexity lies in the LLM classification pipeline with threshold calibration and the data preprocessing. The paper does not provide code, making full reproduction dependent on implementing the described algorithms from scratch.

The methodology is well-described with clear formulas for RVA, GCI, and ETGCI computation. However, the primary data source (Crunchbase) is a commercial database requiring subscription. The LLM classifier (ChatGPT-4o-mini) is accessible but API-dependent. The 18-domain taxonomy and classification thresholds are specified. Robustness checks (rounding, two-year windows) are documented. No code repository is mentioned. The validation set of 200 startups is described but not publicly shared.