Predicting Startup Success Using Publicly Available Data

<p>Predicting the success of an early-stage startup has always been a major effort for investors and venture funds. Statistically, there are about 305 million total startups created in a year, but less than 10% of them succeed to become profitable businesses. Accurately identifying the signs of startup growth is the work of countless investors, and in recent years, research has turned to machine learning in hopes of improving the accuracy and speed of startup success prediction.</p> <p>To learn about a startup, investors have to navigate many different internet sources and often rely on personal intuition to determine the startup’s potential and likelihood of success. This thesis explores whether online data about a company, particularly general company data, previous funding events, published news articles, internet presence, and social media activity can be used to identify fast-growing startups. Data collected from Crunchbase, the Google Search API, and Twitter was used to predict whether a company will raise a round of funding within a fixed time horizon.</p> <p>A total of ten machine learning models were evaluated and the CatBoost ensemble method achieved the best performance with precision, recall, and F1 scores of 0.663, 0.827, and 0.736 respectively for predicting funding within 3 years. The same ensem- ble method achieved F1 scores of 0.528, 0.683, 0.736, 0.763, and 0.777 at predicting funding 1-5 years into the future. The final objective was to predict whether a startup that had already raised an angel or seed round would raise another investment within a one-year horizon. The CatBoost model with a 0.75 cutoff achieved precision and F0.1 scores of 0.790 and 0.774, beating the results of previous work in this field.</p>

Name thesis:degree_name

MS in Computer Science

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2022

Author dc:creator

• Gavrilenko, Emily

Contributors dc:contributor

• Foaad Khosmood
• Computer Science
• College of Engineering

• AI
• Machine Learning
• Artificial Intelligence
• Startup
• Computer Science
• Artificial Intelligence and Robotics

Identifier

10.15368/theses.2022.119

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-4203