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Driving venture capital funding efficiencies through data driven models. Why is this important and what are its implications for the startup ecosystem?

KAKAR, ASHISH (2024) Driving venture capital funding efficiencies through data driven models. Why is this important and what are its implications for the startup ecosystem? Doctoral thesis, Durham University.



This thesis aims to test whether data models can fit the venture capital funding process better, and if they do fit, can they help improve the venture capital funding efficiency?

Based on the reported results, venture capitalists can only see returns in 20% of their investments. The thesis argues that it is essential to help venture capital investment as it can help drive economic growth through investments in innovation.

The thesis considers four startup scenarios and the related investment factors. The scenarios are a funded artificial intelligence startup seeking follow-on funding, a new startup seeking first funding, the survivability of a sustainability-focused startup, and the importance of patents for exit. Patents are a proxy for innovation in this thesis.

Through quantitative analysis using generalized linear models, logit regressions, and t-tests, the thesis can establish that data models can identify the relative significance of funding factors. Once the factor significance is established, it can be deployed in a model. Building the machine learning model has been considered outside the scope of this thesis.

A mix of academic and real-world research has been used for the data analysis of this thesis. Accelerators and venture capitalists also used some of the results to improve their own processes. Many of the models have shifted from a prediction to factor significance.

This thesis implies that it could help venture capitalists plan for a 10% efficiency improvement. From an academic perspective, this study focuses on the entire life of a startup, from the first funding stage to the exit. It also links the startup ecosystem with economic development. Two additional factors from the study are the regional perspective of funding differences between Asia, Europe, and the US and that this study would include the recent economic sentiment. The impact of the funding slowdown has been measured through a focus on first funding and longitudinal validations of the data decision before the slowdown.

Based on the results of the thesis, data models are a credible alternative and show significant correlations between returns and factors. It is advisable for a venture capitalist to consider these.

Item Type:Thesis (Doctoral)
Award:Doctor of Business Administration
Keywords:Venture capital, asset management, artificial intelligence, machine learning, data models, startup funding
Faculty and Department:Faculty of Business
Thesis Date:2024
Copyright:Copyright of this thesis is held by the author
Deposited On:08 Feb 2024 15:29

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