Determinants of Amount Raised in Equity Crowdfunding Campaigns; an Application of Truncated Regression

Secondary-market data on 1189 crowdfunding campaigns, extracted from the leading UK equity crowdfunding platform Seedrs, is<br/>used to identify the determinants of amount invested. A key feature of the dataset is that only successful campaigns are observed.<br/>For this reason, we depart from previous literature by applying the truncated regression estimator instead of ordinary least squares<br/>regression. We cluster at the company level in order to allow for multiple campaigns per company. The importance of allowing for<br/>truncation is confirmed using the Hausman test. Moreover, allowing for truncation alters the conclusions arising from previous<br/>literature concerning the effects of standard variables, including target amount, amount of equity offered, and number of team<br/>members participating in the venture. Quadratic terms are introduced in order to identify “sweet spots.” Textual dummies are<br/>included in order to identify words that enhance investor appeal. The Linktest misspecification testing procedure confirms the<br/>validity of our final model.<br/>JEL Classification: C24;O3

Publication Details

Journal
Research Explorer (The University of Manchester)
Published
2026-04-01
Primary Topic
FinTech, Crowdfunding, Digital Finance
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article
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article

Determinants of Amount Raised in Equity Crowdfunding Campaigns; an Application of Truncated Regression

Research Explorer (The University of Manchester)
FinTech, Crowdfunding, Digital Finance
article

Determinants of Amount Raised in Equity Crowdfunding Campaigns; an Application of Truncated Regression

article en

Abstract

Secondary-market data on 1189 crowdfunding campaigns, extracted from the leading UK equity crowdfunding platform Seedrs, is<br/>used to identify the determinants of amount invested. A key feature of the dataset is that only successful campaigns are observed.<br/>For this reason, we depart from previous literature by applying the truncated regression estimator instead of ordinary least squares<br/>regression. We cluster at the company level in order to allow for multiple campaigns per company. The importance of allowing for<br/>truncation is confirmed using the Hausman test. Moreover, allowing for truncation alters the conclusions arising from previous<br/>literature concerning the effects of standard variables, including target amount, amount of equity offered, and number of team<br/>members participating in the venture. Quadratic terms are introduced in order to identify “sweet spots.” Textual dummies are<br/>included in order to identify words that enhance investor appeal. The Linktest misspecification testing procedure confirms the<br/>validity of our final model.<br/>JEL Classification: C24;O3

Research Explorer (The University of Manchester)
Openalex Percentile: Top 99%
FinTech, Crowdfunding, Digital Finance
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