Work

Field lab YunoAI: startup analytics-a machine learning analysis of acquisition and IPO likelihood

Year: 2025

Type: dissertation

Abstract: This thesis explores the drivers of startup success through a data-driven, multidimensional approach. First, it examines funding metrics to identify the strongest predictors of outcomes like follow-on rounds, acquisitions, or IPOs. Second, machine learning models analyze failure dynamics, pinpointing critical risk factors to improve failure prediction. Third, founder-market fit (FMF) is quantified using NLP techniques and LinkedIn data, producing an objective FMF score to guide venture capital decisions. Lastly, founder-specific traits—including education, experience, and networks—are assessed through advanced text analysis and personality predictions. By integrating machine learning with entrepreneurial insights, this research provides actionable tools for stakeholders.

Source: Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)

Author: Gonchar, Ekaterina

Language: English

Topic: Private Equity and Venture Capital

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

Funder: Fundação para a Ciência e a Tecnologia

Awards: G1516346500, G3257768443, G3821169523, Nova School of Business and Economics, Nova School of Business and Economics, G946021960

OpenAlex ID: W7159906381

Cites: 36

Cited by:

Related to: 10