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Improve Entrepreneurial Funding Screening and Evaluation: Business Success Prediction with Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improve Entrepreneurial Funding Screening and Evaluation: Business Success Prediction with Machine Learning./
作者:	Pan, Chenchen.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	132 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Feature selection. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29003885
ISBN:	9798209787679

Improve Entrepreneurial Funding Screening and Evaluation: Business Success Prediction with Machine Learning.
Pan, Chenchen.

Improve Entrepreneurial Funding Screening and Evaluation: Business Success Prediction with Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 132 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

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.

ISBN: 9798209787679Subjects--Topical Terms:

3560270
Feature selection.

Improve Entrepreneurial Funding Screening and Evaluation: Business Success Prediction with Machine Learning.
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