Chapter 1 – Setting the Context – Unpacking Alpha in Venture Capital
As a systematic public market investor, venture capital was an asset class that felt opaque to me based on only my limited exposure of angel investing and my friends in the industry. It is a challenging asset class to digest and I am sure a majority of large institutional pools of capital would struggle to list more than a handful of brand name VC firms or investors. That said, top decile returns in VC are extremely impressive so I decided to take a deeper look with a view to create a ecology map and building an institutional footprint from first principles.
I run a systematic hedge fund that I built from first principles to specialise in deep research and quantitative decision-making enabled by cutting edge technology. I studied robotics at Carnegie Mellon. My career led me into the family computing business in the emerging markets then later, via an MBA at Oxford, into finance (both PE and Public Markets). I have always harboured a passion for technology. As an SU alum, my core view on how technology is impacting our society continues to evolve but my cornerstone belief is that there is no sector of the modern economy that technology does not now touch. I am an angel investor with personal investments in some amazing entrepreneurs that have built great businesses such as Azmat (Citymapper) and Robbie (Kyra), and some others who are currently realizing some truly frontier innovations such as James (Lab Genius), Gilad (Tropic Biosciences).
I have looked deeply at the VC asset class, running analysis of historical VC fund returns (+1500 funds globally), digested and parsed a broad array of market and academic research (50+ academic papers published over the last two decades); and completed 100+ expert interviews with a range of top tier GPs, FoFs and entrepreneurs based in the US, EU and Asia.
The Purpose of this Series
As a systematic public market investor, venture capital was an asset class that felt opaque to me based on only my limited exposure of angel investing and my friends in the industry. It is a challenging asset class to digest and I am sure a majority of large institutional pools of capital would struggle to list more than a handful of brand name VC firms or investors. That said, top decile returns in VC are extremely impressive so I decided to take a deeper look with a view to create a ecology map and building an institutional footprint from first principles. The historical narrative for prospective investors into the asset class has been that the only way to access these returns is to invest in brand name funds. Such funds are capacity constrained and are usually closed to new LPs. As a counter narrative, there are continually new funds entering the market and capturing value. This presents a challenge.
Who is this Series For?
I want this to be a piece of content that adds to the narrative on venture capital. I hope this will serve as a solid conversation with practitioners, investors, allocators and founders.
My base hypothesis was that as a pool of capital, an allocation to VC can deliver uncorrelated, strong returns and that there is an informational benefit to having a lens into the future technologies that will continue to displace operations, impair assets, and disrupt incumbents.
The Core Questions I Wanted to Answer Are:
- At the base level, why bother with VC?
- What drives outperformance in the top decile VC funds? What does Alpha in VC look like unbundled and how do you attribute this at the firm level?
- Once unbundled, can you build a top decile VC fund, top down and what are the key decisions points for institutional capital when thinking about this?
- As a public market investor, are early stage technologies informative and do they provide informational alpha. If so, how do you build that informational alpha funnel and does that improve returns across strategies?
Spoiler Alert: My 10 Personal Conclusions on Venture Capital
Whilst not wanting to spoil the suspense of what follows in the series, I thought it would be helpful to lay out my core conclusions on venture capital. Here goes:
- VC is a cottage industry but done scrupulously and systematically it can deliver strong, uncorrelated returns. Alpha generation is very poorly attributed.
- Dollars should be focused into capacity constrained strategies that are attacking the early stages. VC does not scale.
- I see no obvious warning signs that this is a poor time to enter the asset class. Technology-led innovation is pervasive and cumulative.
- Whilst Silicon Valley has undoubtedly been the epicentre of technology innovation, other hubs of ideation, innovation and global problem solving are developing fast.
- VC is a human capital business, driven by prescient GPs and outlier founders. There is limited evidence to support long-term consistent firm-level performance, in fact persistence of performance is declining.
- Investing with more metrics = less alpha. The best investors are comfortable investing at the edges but do so on the basis of a scientific and rigorous process that appreciate the risk. A quick summary of a rigorous methodology is inspired by a recent book from P. Tetlock (Superforecasting).
- The best early stage investors are foxes — they are curious polymaths, with broad peripheral vision. LPs should test for and allocate to investors with the optimal attributes versus making their own editorial about where the tech next wave will come from.
- Technology KPIs have evolved but I believe most public market investors still don’t understand the pervasiveness of technology. Every listed asset is potentially impaired.
- LPs have not challenged their GPs to innovate nor gone deeper on GP level data. I consider the industry must mature faster and both sides must do better.
- Most early stage investors waste the informational alpha generated by VC — it provides a lens into what will work in the future but in nearly every scenario tells you what is not working within the incumbents. Cross-pollinate this information to unlock more alpha in you public portfolios.
Each subsequent Chapter goes deeper into substantiating the above. A snippet of what will follow is:
Chapter 2 — Debunking of VC myths.
Chapter 3 — Unpacking components of Alpha generation in VC
Chapter 4 — VC mindset — Fox vs Hedgehog
Chapter 5 — Hidden informational Alpha
Caveat: What You Should Know About the Data
There are significant challenges with VC performance data and there have been historic inconsistencies in performance capture with pervasive biases (e.g. over-reporting for marketing purposes and certainly a US bias). The four main public databases (Burgiss, Cambridge Associates, Pitchbook & Preqin) are typically based on general press, research teams, disclosures from limited partners, regulatory filings, and other public (but often difficult to access) sources so the sample set is not conclusive. The failings have been noted in the academic arena and initiatives have been launched. That said, my research is based on paid access to Pitchbook and Preqin which represent a solid sample set and I consider sufficient for the purpose of this series.
Acknowledgements and a Personal Request for Feedback
Before I dive in, I do want to thank all the friends that have helped me understand this asset class better. We consider venture capital to be an incredibly collaborative community and it has been a pleasure to learn more deeply this unique community that has created so much value for the broader society. I also want to acknowledge the broad array of both academic and practitioner literature that is available and has been incredibly informative to my process.
I plan to release another four chapters in this series that chronicle my learnings about this asset class. I encourage feedback and constructive criticism from entrepreneurs, investors and other interested parties. Whilst some comments might be considered contentious I want to preface that: (i) I acknowledge the theory in the analysis and a practitioner’s lens will always provide seasoned and sensible counterfactuals; and (ii) there are certain venture capitalists whose artful grasp of the science of the asset class I am deeply in awe of.
I firmly believe in the power of collective learning and hope my research can contribute to the broader discussion.
Disclaimer: All the opinions are my own and do not reflect those of Jetstone Asset Management (UK) LLP. This document is provided for informational and discussion purposes only. It is not a solicitation or an offer to buy or sell any security or other financial instrument. Any information including facts, opinions or quotations, may be condensed or summarised and is expressed as of the date of writing. The information may change without notice. It may not be reproduced either in whole, or in part, without my permission. This document is not marketing material or is not used for the purposes of marketing. Copyright Ahmad Butt © 2018.