[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119194-en":3,"doc-seo-119194-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119194,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Signaling and perceiving on equity crowdfunding decisions - a machine learning approach","This study explores how signaling and perceiving jointly shape crowd investors’ decision-making in equity crowdfunding. Five machine learning models evaluate how different information types predict crowdfunding success. Results show investors favor well-structured quantitative data over complex qualitative content, and quantitative processing is less cognitively demanding than extracting value from text and images. Entrepreneurs’ signaling combined with investors’ processing reduces information asymmetry and highlights investors’ often-overlooked role. The study also tests the policy effect of China’s 2016 Interim Measures on Online Lending by comparing periods of thriving versus constraining crowdfunding growth, offering implications for risk-aware regulation.","Small Bus Econ  \n[https://doi.org/10.1007/s11187-024-00991-3](https://doi.org/10.1007/s11187-024-00991-3)  \nSignaling and perceiving on equity crowdfunding decisions—a machine learning approach  \nJinjuan Yang · Jiayuan Xin · Yan Zeng · Pei Jose Liu  \nAccepted: 4 December 2024  \n© The Author(s) 2025  \nAbstract This study explores how signaling and perceiving jointly influence crowd investors’ decision-making. We utilize five machine learning models to assess the predictive power of various information types on crowdfunding success. Our findings indicate that investors prioritize well-structured quantitative data over complex qualitative content. Processing quantitative information is also found to be less cognitively taxing than extracting useful information from qualitative text and images. Entrepreneurs’ signaling and investors’ processing jointly reduce information asymmetry in crowdfunding, highlighting the critical yet often-overlooked role of investors’ information processing. Additionally, we test the policy effect of the ‘2016 Interim Measures on Online Lending’ on crowdfunding success by comparing the predictive accuracy of information during the thriving and constraining periods of crowdfunding development in China. Our results have significant implications for policymakers that crowdfunding fosters economic growth by connecting entrepreneurs and investors and  \nJ. Yang  \nSchool of Business and Management, Shanghai International Studies University, Shanghai, China  \nJ. Xin · P. J. Liu (*)  \nNewcastle University Business School, Newcastle University, Newcastle Upon Tyne, UK [e-mail: pei.liu@newcastle.ac.uk](e-mail: pei.liu@newcastle.ac.uk)  \n[Y. Zeng](Y. Zeng)  \nFaculty of Business and Law, University of the West of England, Bristol, UK  \nshould not be halted due to risks, especially during periods of financial constraints.  \nPlain English Summary This study examineshow the information shared by entrepreneurs and processed by investors impacts crowdfunding success. Using five machine learning models, we find that investors prioritize clear, well-structured quantitative data, as it is easier to process than text or images. Presenting information in a way that reduces cognitive effort helps bridge the gap between entrepreneurs and investors. We also analyze the impact of China’s ‘2016 Interim Measureson Online Lending,’ finding that while regulations stabilize crowdfunding, shutting platforms down harms economic growth, particularly during financial instability. The principal implication of this study is that policymakers should regulate crowdfunding to address risks without stifling its potential. Crowdfunding platforms provide essential funding opportunities for small business entrepreneurs, especially in constrained financial environments.  \nKeywords Equity crowdfunding · Information asymmetry · Dual-system · Cognitive processing · Machine learning  \nJEL Classification G11 · G21 · G32 · D82  \n1 Introduction  \nStartups often struggle to secure financing from traditional banks due to their early-stage and uncertain nature. For decades, Silicon Valley Bank (SVB) played a pivotal role in bridging this funding gap, supporting nearly half of all the U.S. venture-backed firms and significantly contributing to the growth of the technology industry both domestically and internationally. The sudden collapse of SVB has left a considerable void in startup financing, raising concerns among founders and investors about the survival of high-potential startups in an already constrained funding environment. In this context, equity crowdfunding, 1 has emerged as an alternative source of capital, enabling unlisted startups to raise funds from a broader base of investors (Cumming et al., 2019b; De Crescenzo et al., 2020 ; Wang et al., 2019) . However, a critical challenge in crowdfunding investment lies in the high-noise operating environment, where entrepreneurs often present a bundle of diverse signals to attract potential backers (Cou","cbCair0wXHDSRlyM","https://ap.wps.com/l/cbCair0wXHDSRlyM","pdf",1575926,1,42,"English","en",105,"# Abstract\n# Keywords\n# JEL Classification\n# Introduction","[{\"question\":\"How do signaling and investor perceiving jointly influence equity crowdfunding decisions?\",\"answer\":\"The study shows that entrepreneurs’ signaling and investors’ information processing interact to reduce information asymmetry, which in turn affects crowdfunding success.\"},{\"question\":\"Which types of information best predict crowdfunding success?\",\"answer\":\"Investors prioritize clear, well-structured quantitative data, which also proves less cognitively taxing to process than extracting useful meaning from qualitative text and images.\"},{\"question\":\"What is the role of the “2016 Interim Measures on Online Lending” in the study’s results?\",\"answer\":\"By comparing thriving and constraining periods, the study finds that regulations stabilize crowdfunding, but shutting platforms down can harm economic growth, especially during financial instability.\"}]","Signaling and perceiving on equity crowdfunding decisions - 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