Equity Crowdfunding Synthetic Dataset (2020–2026)
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| Format: | Recurso digital |
| Sprache: | Englisch |
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Zenodo
2026
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| _version_ | 1866901843632717824 |
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| author | Sáez-Ortuño, Laura Forgas-Coll, Santiago Sánchez-García, Javier Sagarra, Marti Huertas-Garcia, Ruben |
| author_facet | Sáez-Ortuño, Laura Forgas-Coll, Santiago Sánchez-García, Javier Sagarra, Marti Huertas-Garcia, Ruben |
| contents | <div> <p>This dataset contains a fully synthetic sample of 3,000 equity crowdfunding campaigns conducted between 2020 and 2026. It has been generated for research, teaching, and methodological experimentation in the fields of entrepreneurial finance, crowdfunding markets, and innovation studies.</p> <p>The dataset includes detailed variables describing campaign, company, and platform characteristics: sector, country, platform, round type, financial instrument, valuations, target amount, amount raised, equity offered, overfunding ratio, fees, campaign duration, number of investors, minimum and average investment ticket, as well as post‑money valuation. Additional derived indicators (such as implied ROI, investor conversion rates, and estimated campaign visits) are included to facilitate empirical modelling, benchmarking, and advanced analytical exercises.</p> <p>All observations in this dataset are entirely <strong>synthetic</strong>. The data were generated using probabilistic modelling, distributional assumptions calibrated to realistic market behaviour, and controlled noise injection. No real individuals, companies, or crowdfunding campaigns are represented. As such, the dataset is safe for open dissemination and can be used for reproducible research, methodological development, simulations, and educational purposes.</p> <p>Potential applications include:<br>• modelling success and overfunding determinants in equity crowdfunding,<br>• analysing investor dynamics and ticket size distributions,<br>• benchmarking platform performance across countries and sectors,<br>• teaching data science and econometrics using realistic financial datasets,<br>• developing and testing machine learning models for campaign outcome prediction.</p> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18846917 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Equity Crowdfunding Synthetic Dataset (2020–2026) Sáez-Ortuño, Laura Forgas-Coll, Santiago Sánchez-García, Javier Sagarra, Marti Huertas-Garcia, Ruben <div> <p>This dataset contains a fully synthetic sample of 3,000 equity crowdfunding campaigns conducted between 2020 and 2026. It has been generated for research, teaching, and methodological experimentation in the fields of entrepreneurial finance, crowdfunding markets, and innovation studies.</p> <p>The dataset includes detailed variables describing campaign, company, and platform characteristics: sector, country, platform, round type, financial instrument, valuations, target amount, amount raised, equity offered, overfunding ratio, fees, campaign duration, number of investors, minimum and average investment ticket, as well as post‑money valuation. Additional derived indicators (such as implied ROI, investor conversion rates, and estimated campaign visits) are included to facilitate empirical modelling, benchmarking, and advanced analytical exercises.</p> <p>All observations in this dataset are entirely <strong>synthetic</strong>. The data were generated using probabilistic modelling, distributional assumptions calibrated to realistic market behaviour, and controlled noise injection. No real individuals, companies, or crowdfunding campaigns are represented. As such, the dataset is safe for open dissemination and can be used for reproducible research, methodological development, simulations, and educational purposes.</p> <p>Potential applications include:<br>• modelling success and overfunding determinants in equity crowdfunding,<br>• analysing investor dynamics and ticket size distributions,<br>• benchmarking platform performance across countries and sectors,<br>• teaching data science and econometrics using realistic financial datasets,<br>• developing and testing machine learning models for campaign outcome prediction.</p> </div> |
| title | Equity Crowdfunding Synthetic Dataset (2020–2026) |
| url | https://doi.org/10.5281/zenodo.18846917 |