Start-ups: Evaluation by VCs based on machine learning algorithms

💡 Background

Your thesis will be part of a research project focused on “predicting start-up success”. Especially in the early phases of start-up creation, there are no objective criteria for evaluating start-ups. Although there is consensus that team dynamics and personality are predictors for start-up success in these early phases, there is only little empirical research that has studied these factors as predictors for start-up success.

🦾Who We Are

The Chair for Strategy and Organization is focused on research with impact. This means we do not want to repeat old ideas and base our research solely on the research people did 10 years ago. Instead, we currently research topics that will shape the future. Topics such as Agile Organizations and Digital Disruption, Blockchain Technology, Creativity and Innovation, Digital Transformation and Business Model Innovation, Diversity, Education: Education Technology and Performance Management, HRTech, Leadership, and Teams. We are always early in noticing trends, technologies, strategies, and organizations that shape the future, which has its ups and downs.

🎯 Goals

The goal is to analyze current academic research on machine learning algorithms, applied for the purpose of start-up evaluation by VCs or early stage investors. Further, a viewpoint from the practitioners side is desirable. The scope will be determined based on your background and type of thesis / project study.

Explicit deliverables will include:

• Overview of current research on machine learning in VCs (~within 1. month)
• List of start-up variables used to train machine learning algorithms (~within 1. month)
• Overview of machine learning algorithm types used (~within 1.-2. month)
• Depending on academic background (business vs. IT): trained machine learning algorithms (~within 2.-4. month)

🧠 Topics of Interest

• Data analysis
• Machine learning
• Start-ups / entrepreneurship
• Venture capitalists / investors

🎓 Profile

• Reliable and self-driven
• Enthusiasm for start-ups
• Ability to do internet and desk research as well as connect with practitioners
• Passion to learn more about the future and do research with impact

📚 Further Reading

• Catalini, C., Foster, C., & Nanda, R. (2018). Machine intelligence vs. human judgement in new venture finance. Academy of Management Proceeding
• Corea, Francesco; Bertinetti, Giorgio; Cervellati, Enrico Maria (2021): Hacking the venture industry: An Early-stage Startups Investment framework for data-driven investors. In Machine Learning with Applications 5, p. 100062. DOI: 10.1016/j.mlwa.2021.100062
• Retterath, Andre (2020): Human Versus Computer: Benchmarking Venture Capitalists and Machine Learning Algorithms for Investment Screening
• Arroyo, Javier; Corea, Francesco; Jimenez-Diaz, Guillermo; Recio-Garcia, Juan A. (2019): Assessment of Machine Learning Performance for Decision Support in Venture Capital Investments. In IEEE Access 7 (99), pp. 124233–124243. DOI: 10.1109/ACCESS.2019.2938659

Super Founders: What Data Reveals About Billion-Dollar Startups - By Ali Tamaseb

Ali Tamaseb has spent thousands of hours manually amassing what may be the largest dataset ever collected on startups, comparing billion-dollar startups with those that failed to become one-30,000 data points on nearly every factor.

www.superfoundersbook.com

A machine-learning approach to venture capital

Veronica Wu has been in on the ground floor for many of the dramatic technology shifts that have defined the past 20 years. Beijing-born and US-educated, Wu has worked in top strategy roles at a string of major US tech companies-Apple, Motorola, and Tesla-in their Chinese operations.

www.mckinsey.com

MACHINE LEARNING-ASSISTED VENTURE CAPITAL

The brain is one of the most complex organs in the human body. Yet, even with all its intricacies, the human brain does not fare well in processing large amounts of information.

medium.datadriveninvestor.com

Using Machine Learning In Venture Capital

If you have read some of my previous posts, you may know I am very bullish on data-driven funds. The rationale for my optimism is that I fundamentally believe that machine learning can bridge the asymmetric information gap between founders and investors, making both of their lives better and easier.

www.forbes.com

📄 Requirements to any Work

We do not want your research to gather dust in some corner of bookshelf but make it accessible to the world. Thus, we warmly encourage you to create some or all of the following:

• Infograph - visually represent some of your work (find examples here)
• Slide Deck - summarize your research and possibly present it
• Extract most important sequences from podcasts, videos, and other media
• 3-4 Tweets about the most important findings and summarizing the topic
• optional: Medium Article - let people outside the university know about your research and start your personal brand

📬 How to Apply

If you are interested, please contact Riccarda Joas (e-mail below) by submitting your CV and grade report. Please also briefly outline your experience/knowledge and - if applicable - your tentative research idea (research question, methods and data, possible outcomes with a tentative outline all in word as *.docx)

We're greatly looking forward to hearing more about you!

👉 riccarda.joas@tum.de