arXiv subjects

Jean-Michel Dalle

Publications and source records attributed to Jean-Michel Dalle.

6 recordsLinked to original sources

arXiv2026

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

We explore a quantitative approach to emerging technological sovereignty and geoeconomic power by assessing the relative positioning of countries with economic complexity methods applied to the structure of national venture-capital (VC) portfolios and their associated Revealed Venture Advantage (RVA) metrics. Using Crunchbase firm- and deal-level data, we map venture-backed startups to 18 emerging technology domains via a probabilistic multi-label large-language-model classifier, and construct an RVA-based country-technology specialization matrix for the 17 countries with the highest aggregate VC funding. From this matrix, we derive two eigenvector-based measures: a Geoeconomic Complexity Index (GCI) that ranks countries by the composition of their venture specializations, and an Emerging Technology Geoeconomic Complexity Index (ETGCI) that ranks domains by the extent to which specialization is concentrated among high-GCI countries. Empirically, Cloud Computing, Cybersecurity Tools, and Medtech exhibit the highest ETGCI values, reflecting concentration of specialization in a small set of leading countries. The United States and Israel consistently occupy a marked "high-diversity/low-ubiquity" position and lead the GCI ranking, followed by China, France, Japan, and Germany; both country and domain rankings are stable from 2021-2024. Finally, relatedness-based simulations identify, when it exists, for each country the Simplest Single Sovereignty Enhancing Technology (SSSET), i.e., the most feasible single new technological direction associated with the largest expected improvement in relative geoeconomic positioning.

econ.GN↗

arXiv2023

Investor-patent networks as mutualistic networks

Venture capital investments in startups have come to represent an important driver of technological innovation, in parallel to corporate- and government-directed efforts. Part of the future of artificial intelligence, medicine and quantum computing now depends upon a large number of venture investment decisions whose robustness against increasingly frequent crises has therefore become crucial. To shed light on this issue, and by combining large-scale financial, startup and patent datasets, we analyze the interactions between venture capitalists and technologies as an explicit bipartite patent-investor network. Our results reveal that this network is topologically mutualistic because of the prevalence of links between generalist investors, whose portfolios are technologically diversified, and general-purpose technologies, characterized by a broad spectrum of use. As a consequence, the robustness of venture-funded technological innovation against different types of crises is affected by the high nestedness and low modularity, with high connectance, associated with mutualistic networks.

physics.soc-ph↗

arXiv2020

Towards Entrepreneurial Ecosystem Indicators : Speed and Acceleration

We suggest the use of indicators to analyze entrepreneurial ecosystems, in a way similar to ecological indicators: simple, measurable, and actionable characteristics, used to convey relevant information to stakeholders and policymakers. We define 3 possible such indicators: Fundraising Speed, Acceleration and nth-year speed, all related to the ability of startups to develop more or less rapidly in a given ecosystem. Results based on these 3 indicators for 6 prominent ecosystems (Berlin, Israel, London, New York, Paris, Silicon Valley) exhibit markedly different situations and trajectories. Altogether, they contribute to confirm that such indicators can help shed new and interesting light on entrepreneurial ecosystems, to the benefit of potentially more grounded policy decisions, and all the more so in otherwise blurred and somewhat cacophonic environments.

econ.GN↗

arXiv2019

The varying importance of extrinsic factors in the success of startup fundraising: competition at early-stage and networks at growth-stage

We address the issue of the factors driving startup success in raising funds. Using the popular and public startup database Crunchbase, we explicitly take into account two extrinsic characteristics of startups: the competition that the companies face, using similarity measures derived from the Word2Vec algorithm, as well as the position of investors in the investment network, pioneering the use of Graph Neural Networks (GNN), a recent deep learning technique that enables the handling of graphs as such and as a whole. We show that the different stages of fundraising, early- and growth-stage, are associated with different success factors. Our results suggest a marked relevance of startup competition for early stage while growth-stage fundraising is influenced by network features. Both of these factors tend to average out in global models, which could lead to the false impression that startup success in fundraising would mostly if not only be influenced by its intrinsic characteristics, notably those of their founders.

q-fin.GN↗

arXiv2019

The emerging sectoral diversity of startup ecosystems

Thanks to the recent availability of comprehensive and detailed online databases of startup companies, it has become possible to more directly investigate startup ecosystems i.e. startup populations in specific regions. In this paper, we analyze the emergence of 20+ such ecosystems in Europe and the USA, with a specific focus on their sectoral diversity. Analyzing the sectoral landscapes of these ecosystems using a new visualization tool indeed highlights marked differences in terms of diversity, which we characterize using metrics derived from ecological sciences. Numerical simulations suggest that the emerging diversity of startup ecosystems can be explained using a simple preferential attachment model based on sectoral funding.

physics.soc-ph↗

arXiv2019

The temporal evolution of venture investment strategies in sector space

We analyze the sectoral dynamics of startup venture financing. Based on a dataset of 52000 start-ups and 110000 funding rounds in the United States from 2000 to 2017, and by applying both Principal Component Analysis (PCA) and Tensor Component Analysis (TCA) in sector space, we visualize and measure the evolution of the investment strategies of different classes of investors across sectors and over time. During the past decade, we observe a coherent evolution of early stage investments towards a lower-tech area in sector space, associated with a marked increase in the concentration of investments and with the emergence of a newer class of investors called accelerators. We provide evidence for a more recent shift of start-up venture financing away from the previous one.

q-fin.GN↗