Driving venture capital funding efficiencies through data-driven models
Ashish KAKAR (2024) - Doctoral thesis
Driving venture capital funding efficiencies through data driven models. Why is this important and what are its implications for the startup ecosystem?
Research context and problem
Venture Capital (VC) is a vital engine for economic growth, yet its decision-making processes remain surprisingly traditional. While VCs fund cutting-edge technologies, they often rely on heuristics and intuition, leading to a stark reality where 80% of investments fail to yield returns. This research addresses this gap, exploring how the industry can transition toward data-driven, algorithmic models to enhance funding efficiency in an era of Big Data and AI.
Research objectives
The primary goal of this thesis is to test whether data-centric models can better fit the venture capital funding process and improve its overall efficiency. It aims to identify the relative significance of various funding factors across the startup lifecycle, from seed stage to exit, providing a structured framework for both investors and entrepreneurs.
Methodological approach
The study employs a quantitative analysis using generalized linear models and logit regressions on real-world datasets. It examines four distinct startup scenarios, focusing on high-growth sectors like Artificial Intelligence and Sustainability. The research also provides a comparative regional analysis across the US, Europe, and Asia to understand geographical nuances in funding factors.
Key findings and contributions
The research demonstrates that data models are a credible alternative to traditional methods, showing significant correlations between specific factors and investment returns. Key findings include:
- Regional differences play a crucial role; factors successful in one market may be suboptimal in another.
- Firm size (number of employees) is a significant predictor for follow-on funding in AI startups, signaling scaling capability.
- Patents serve as critical proxies for innovation, directly impacting both funding success and exit potential. The thesis suggests that adopting these models could help VCs achieve a 10% improvement in funding efficiency.
Managerial and societal implications
For VCs, this study provides the tools to move away from "gut-feeling" decisions toward evidence-based risk management. For founders, it highlights the essential features required to attract investment. Ultimately, increasing VC efficiency ensures that capital is more effectively allocated to innovations that drive productivity and economic development.
Conclusion
By bridging the gap between entrepreneurial finance and data analytics, this thesis offers a transformative perspective for the startup ecosystem, providing a robust framework for improving investment outcomes and fostering global innovation.
The full thesis is available online on the Durham University website.