What makes successful equity crowdfunding campaigns? A machine learning analysis of information cues
本研究结合信号理论与信息线索理论，用机器学习分析中国平台144个众筹项目，发现定量信息最能预测成功，叙事线索次之，视觉线索作用有限；团队规模大、目标金额高、叙事简洁清晰的项目更易成功。
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.