Improve entrepreneurial funding screening and evaluation : business success prediction with machine learning
Abstract/Contents
- Abstract
- As entrepreneurial funding supports the growth of entrepreneurial firms, it can be viewed as the fuel that enhances the creation, development, and growth of new technologies, industries, and markets. However, investing in entrepreneurial firms is highly risky. For example, Venture Capitalists (VCs) may receive a large number of business plans/proposals every year but only a few of them can be successful. Since VCs typically employ a small number of people, they do require a more effective and efficient screening and evaluation process. Prior research has mainly focused on identifying evaluation criteria to help VCs predict the business success of these firms. Nevertheless, relying on VCs' self-reporting and small regional datasets, these earlier studies have little agreement on the evaluation criteria. Therefore, it is difficult to employ those criteria to predict business success in practice. In this work, we propose data-driven approaches using machine learning methods as a complementary methodology to help VCs predict companies' success when they screen and evaluate investment deals. We start by verifying new evaluation criteria with large datasets and then focus on applying machine learning methods to predict business success. We compare different machine learning methods and discuss how VCs can benefit from the prediction. We also apply deep neural networks and few-shot learning methods to two challenging scenarios faced by the VCs: (1) when the companies are just founded and don't have any funding history; (2) when the companies are from an emerging industry that doesn't have a lot of historical data to learn from.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Pan, Chenchen |
|---|---|
| Degree supervisor | Tse, Edison |
| Thesis advisor | Tse, Edison |
| Thesis advisor | Eesley, Charles |
| Thesis advisor | Ye, Yinyu |
| Degree committee member | Eesley, Charles |
| Degree committee member | Ye, Yinyu |
| Associated with | Stanford University, Department of Management Science and Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Chenchen Pan. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/ps743yv6430 |
Access conditions
- Copyright
- © 2021 by Chenchen Pan
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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