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Lookup NU author(s): Dr Doris Xin
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
This study innovatively integrates signaling theory with information cue theory to examine the diagnosticity of various information cues in predicting equity crowdfunding success. Analyzing data from 144 campaigns on China's Dreammove platform (2014-2019) through five machine learning models, we find that quantitative information cues are most predictive of funding success. Narrative cues also hold diagnostic value, albeit to a lesser extent, while visual cues are limited in their predictive capacity. Employing Shapley Additive exPlanations (SHAP) interpretability, we identify larger entrepreneurial teams, ambitious funding targets, concise, accessible narratives, and modest visual elements like youthfulness and clarity enhance campaign appeal. This research advances the understanding of how investors process information in equity crowdfunding, offering new insights by moving beyond single-dimensional analyses and providing practical guidance for entrepreneurs and investors in emerging crowdfunding markets.
Author(s): Yang J, Zeng Y, Xin J, Lin Z, Chen X
Publication type: Article
Publication status: Published
Journal: The European Journal of Finance
Year: 2025
Pages: epub ahead of print
Online publication date: 11/11/2025
Acceptance date: 07/10/2025
Date deposited: 27/11/2025
ISSN (print): 1351-847X
ISSN (electronic): 1466-4364
Publisher: Taylor and Francis
URL: https://doi.org/10.1080/1351847X.2025.2585952
Data Access Statement: Data is available upon request.
