Determinants of Amount Raised in Equity Crowdfunding Campaigns: An Application of Truncated Regression

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

Authors

Institutions

Publication Details

Journal
Bulletin of Economic Research
Published
2025-11-11
DOI
https://doi.org/10.1111/boer.70015
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Determinants of Amount Raised in Equity Crowdfunding Campaigns: An Application of Truncated Regression

Peter G. Moffatt, Simon Peters, Xiaoxu Ma
Bulletin of Economic Research
FinTech, Crowdfunding, Digital Finance
article

Determinants of Amount Raised in Equity Crowdfunding Campaigns: An Application of Truncated Regression

Peter G. Moffatt, Simon Peters, Xiaoxu Ma
article en

Abstract

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

Bulletin of Economic Research
University of East Anglia (GB), University of Manchester (GB)
Openalex Percentile: Top 47%
FinTech, Crowdfunding, Digital Finance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.