Rasivisuth, Pornpanit;
(2025)
Early-Stage Venture Financing: A Data-Driven Approach with Machine Learning Application.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Venture capital (VC) and private equity (PE) become indispensable financial assets, globally driving economic and societal growth. This project primarily aims to investigate the utility of alternative datasets and machine learning models in addressing various challenges prevalent within private markets. These challenges, often exacerbated by the illiquid nature of private investments, information asymmetry, and potential moral hazard issues, include the valuation of Initial Coin O!ering (ICO) tokens, the assessment of early-stage company valuations, and the selection of startups for venture capital funds. A Natural Language Processing (NLP) model, capable of analysing unstructured text data, is employed alongside additional signals derived from alternative data sources such as social media and financial news. Overall, the project is anticipated to benefit academic researchers and practitioners within the private capital sectors, contributing to recent advancements in both the technology and sustainable finance domains.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Early-Stage Venture Financing: A Data-Driven Approach with Machine Learning Application |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2025. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10212237 |
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