Predicting Startup Exit from Textual Descriptors - A Computational Linguistics Framework — Paper

Abstract

This study shows that textual descriptors alone can predict early-stage startup success, defined as Exit, without relying on contextual, financial, or human capital variables. Using venture capital-curated datasets covering 7,419 startups over 20 years, the research isolates text-based framing variables and engineers 850 features through startup narrative mapping. Data subsets and vector embeddings are evaluated for statistical significance, followed by supervised machine learning experiments across six models. LightGBM achieved the highest predictive performance (F1 = 0.48), while textual descriptors alone achieved F1 = 0.30, confirming the standalone predictive value of founder narratives. Feature analysis shows that optimized densities of hyping markers, including adjectives, jargon, and buzzwords, are associated with higher Exit probability, whereas excessive statement or name length reduces it. The study also introduces a quantifiable Hyping Score for venture capital applications, demonstrating that startup framing provides measurable signals for predicting Exit under conditions of high information asymmetry.

Authors 2

Alberto M. G. Saruggia, Sebastien Germano

Specification

Official page: arxiv.org/abs/2608.00045
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
arXiv id: 2608.00045
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
Categories: cs.CL, econ.GN, q-fin.EC
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
PDF: arxiv.org/pdf/2608.00045
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
Primary category: cs.CL
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh

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T19

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7 h ago

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Authors: Alberto M. G. Saruggia, Sebastien Germano

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Value	Valid from → to	Status	Source	Confidence	Extractor
Alberto M. G. Saruggia, Sebastien Germano	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperPredicting Startup Exit from Textual Descriptors - A Computational Linguistics Framework
New paper: Predicting Startup Exit from Textual Descriptors - A Computational Linguistics Framework
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arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CL	feed	T1· Official	5 h ago	1

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