Tag: Fundraising

  • It’s all about identifying outliers

    It’s all about identifying outliers

    What startup investors can learn from sports betting

    Early stage investing is a complex and relatively new practice, which makes it fertile ground for analogies which can help explain the more abstract concepts to both newcomers and veterans alike. 

    In this particular case, grappling with the intrinsic value of pre-revenue startups, there’s an interesting parallel to sports betting. Fundamentally, both involve looking at the strength of a team and the competitive landscape and making a judgement on future potential.

    What we’re considering here is the idea that a startup – even a pre-revenue startup – has a determinable value even before that value has been tested in the form of a market transaction. This is also what you might call a ‘fair market’ valuation, which is what we aim for at Equidam. 

    MOIC vs. betting odds

    In early stage investing, investors will look to benchmark potential returns using a metric called the multiple of invested capital (MOIC). MOIC is calculated by taking the total potential return on an investment and dividing it by the amount of money invested. For example, with an investment of $100,000 in a company with an expected MOIC of 10, the company should have the potential to return $1M.

    In sports betting, participants measure their potential returns using the odds of winning, which represents the probability of success. For example, if the odds of a team winning a basketball game are 9:1, it means the team is assumed to have a 10% chance of winning and the return would be a multiple of 10. 

    Rewarding the earliest participants

    In both examples, the earliest (successful) participants receive the most lucrative returns. In investing, this is because early investors are able to get a lower share price than later investors. In sports betting, this is because early participants are able to secure better odds.

    In both cases, this is for the same reason: At the very beginning there is the least available evidence to indicate an assumed outcome, thus a greater level of perceived risk associated with the choice. This is true both in terms of signals from other participants (other bets or investments made) as well as actual progress in terms of milestones achieved, such as games won or revenue secured. 

    Qualitative and quantitative measures

    In early stage investing, investors use a variety of qualitative and quantitative measures to judge the potential of a company. Qualitative measures might include the management team, strategic relationships, and the competitive environment. Quantitative measures include things like the company’s projected financial performance, market growth and associated risk. This is reflected in the form of the valuation, which ultimately informs the potential return on investment.

    In sports betting, participants use similar measures to judge the potential of a team. That might include the team’s roster, their experience together, track record of the coach, and the threat posed by other teams. This is reflected in the form of a perspective on what the betting odds should be to provide appropriate upside for that level of risk.  

    What this means for early stage investors

    According to some, a startup does not have a valuation until it has been priced in an equity transaction. To an extent (in a strict and formal sense) that is correct. It does not itself have a valuation, because value is not an objective concept. Like beauty, it lies in the eye of the beholder. However, we shouldn’t pretend that an equity transaction represents an ‘objective’ read on value either; it’s also just the opinion of an investor.

    What this analogy illustrates is that you, as an early stage investor, should have your own personal read on valuation as a reflection of future potential. You need to understand the qualitative and quantitative factors involved, and determine a practical framework to run your own analysis. It’s the best way to sharpen your judgement on future potential, take an informed perspective on risk vs return, and put your money to better use.

    If you are investing your own money, it’s not crucial that your valuation framework be seen as objective or fair. Many investors look at valuation primarily in terms of market context and what other investors are doing. Others use simple heuristics like national averages adjusted with a few qualitative measures, even if that screens out some deals. Whatever approach you use, if it allows you to reach your desired level of returns then it is clearly working. 

    When being objective is important 

    Imagine it’s January 2023, and a friend is looking to place a bet on the upcoming NBA championship, and you – being wise and well informed – recommend putting money on the Denver Nuggets. This is a team which hasn’t won a championship in its 47 year history, and a year ago they lost in the first round, so the odds are great (in terms of MOIC) but your friend will take some convincing. 

    It’s in explaining this opportunity to another person that objectivity becomes important; your rationale must survive without the support of your own biases and perceptions. What are the data points which conclude that the Denver Nuggets have been overlooked or undervalued by the market? What is it about their 2023 roster and the wider competitive environment which indicates for success? How do you piece that into a compelling story for your friend? 

    This is, again, mirrored in the world of early stage investment. If you are looking for input on the potential of a startup – which has not yet been rubber-stamped in a market transaction – you will want to see it in a transparent, objective format which covers all of the key indicators. This is applicable in a range of cases, whether that is determining a valuation for the first round of a company, proposing a valuation to a group of angel investors, or reporting updated valuations to your LPs.

    This is where we finally arrive closer to assigning a ‘fair valuation’ to a startup, rather than the individual perspectives on valuation. Not a number determined by the combination of gut-feel and Excel-gymnastics designed to pattern-match past success, but something scrutable, explainable and repeatable. 

    Crucially, valuation can be incredibly useful even when it’s not associated with a market transaction. In fact, the single perspective of a lead investor on the value of a company is potentially less valid, and less reliable, than a more objective framework.1

    Backing outliers is the whole ballgame

    Early stage investment pivots around uncertainty and valuation is always a tricky exercise in assessing the tangible and the intangible. Reaching 100% efficiency in the risk/reward is never going to happen. 

    Similarly, your friend doesn’t have to buy every data point in your recommendation, they just have to understand what you are looking at, the conclusions drawn, and appreciate that it was a rational process with an outcome they can challenge or disregard as they wish. 

    Had they made their bet based on the odds at the start of the season, following the favourite as indicated by the market, they’d have lost their money. Had they waited a few months to see how each team performed in order to inform their judgement, they would have increased their chance to pick the right team, but with much lower potential returns.

    And that’s the argument in a nutshell. In order to understand an opportunity while the terms are most favourable, or to explain that opportunity to others, you need to think about practical and objective measures of future potential. Early stage investing is all about identifying outliers, like the Nuggets, which is precisely why we approach valuation from this perspective. 

    1. Especially given the extreme proclivity of investors to pass the buck, and base their pricing on other market transactions. [↩]
  • Startups are the clients of Venture Capital

    Startups are the clients of Venture Capital

    As a founder learning the ropes of venture capital, you might see VCs as asset managers, with LPs as their customers and your equity as the asset being managed.

    This is heavily implied by the chain of responsibility: you are required to report your progress to your VC investors who want to see milestones crossed and targets met. Similarly, VCs then have to report on the fund’s investments to their LPs. 

    It would be an odd relationship if customers were accountable to service providers, right? 

    Understanding the relationships

    When an LP commits to investing in a VC fund, they are typically locking themselves in for a ten year relationship. That’s three to four years over which they expect their capital to be invested, and six to seven years in which they hope they’ll start to realize those returns. This mirrors the kind of relationship you will have with a VC, which lasts a similar period of time from investment to exit. 

    In this context you might understand that LPs don’t really resemble a customer, and neither do VCs. Instead, they are the shareholders and operators of a specialized financing instrument for early stage companies. The relationship matters, updates are intended to prompt feedback, and success is shared. Crucially, both parties rely on the firm building a reputation for offering a good service, fair terms, and accelerating success stories. 

    Fred Wilson of Union Square Ventures shared more of the VC perspective on this in his 2005 blog post:

    The entrepreneur creates the value, they are the ‘raw material’ in the venture capital business.  If there were no entrepreneurs, there would be no venture capital business.

    Fred Wilson of Union Square Ventures, in “The VC’s Customer“

    How this shapes fundraising

    It can be difficult to view this from the perspective of a founder, as it assumes you are in a position of control – an odd fit with the usual perception of the venture capital process. Isn’t fundraising all about struggling with an endless string of rejections? Again, the bigger picture allows us to see how the relationships really function.

    Healthy markets rely on consumers having freedom of choice, and this is where venture capital suffers from an image problem: When you’re hammered with messaging about how slim the odds are for success, it can seem like raising money from a top-tier firm is the most important signal for success. The moth-like attraction to the top of the market means those firms are swamped with pitches and thus issue even more rejections.1

    However, if you look at venture capital as a marketplace of firms looking to service startups, you might be more inclined to think in terms of practical comparison. Ignoring the logos, who can best serve your particular needs? Where are the hidden gems and less obvious bargains?

    Consider consumer brands, where bigger companies tend to be worse at serving more specific (more technical, higher performance) consumer needs. A larger target market implies more mainstream use cases, and your brand often becomes more important than the performance of your products. At that point, there are likely to be smaller brands that outperform in a particular niche where their expertise makes a difference.

    This is a reasonable metaphor for venture capital, illustrating the benefit of approaching fundraising as a customer looking for a solution rather than an entrepreneur with their hat in their hand. As with any transaction, you are looking for the best bang for your buck, and smaller specialist funds are likely to deliver exactly that – for all sides of the transaction. 

    Highly specialist portfolios from young firms have a top-quartile hit rate of 61%, representing a 2x increase from the most generalist portfolios.

    Liam Shalon of Level Ventures in “Outperformance in Early-Stage Specialist Firms: A Data-driven Analysis“
    Photo by Victoriano Izquierdo on Unsplash

    1. And often lose focus on the fundamental role of VCs: financing innovation, not shaping the future. [↩]
  • LPs should encourage VC evolution

    LPs should encourage VC evolution

    In a previous article I wrote about the threat of consensus in venture capital.

    A few days later, Eric Tarczynski shared a fascinating thread about the journey with Contrary, his VC firm. He addressed this point about consensus with admirable candour, summarised here in two points:

    1. Raising from LPs is easier if you have recognisable logos attached to your previous funds. Success is measured by which big names in VC co-invested with you.
    2. Raising from LPs is easier if they get good references from their existing VCs. So you send deals to them, network with them, and co-invest with them. Success is measured by relationships.

    It’s unusual to get such an unvarnished look at the inside workings of venture capital, and the thread elicited a number of reactions. Most agreed it was a tough pill to swallow:

    Eric’s awesome but boy is that thread a pretty damning look into the inside-baseball-nepotism that starts from the top (LPs) and infects the whole VC ecosystem.

    Luke Thomspon [source]

    ‘We thought that being good investors with a unique thesis that actually makes money would be the best strategy, turns out, following the herd, piling onto garbage, and being unquestioning vassals to incumbent investor power gets you a larger fund’ – My interpretation

    Del Johnson [source]

    There’s an elephant in the room in all of this. Or perhaps it’s a bull in a china shop. Either way, everyone seems to be ignoring it and it’s doing a lot of damage.

    Weak signals

    From pre-seed to IPO, there is no consistent, transparent measure of success. That’s a long time for a GP to deploy capital without any concrete metrics for success. How does an LP ascertain if their money is being put to good use?

    Samir Kaji of Allocate (and former SVB MD) shared his take on the problem that LPs face:

    LPs are programmed to use past track record as the primary driver in making a decision on whether to invest in a new fund (A recent study showed historical persistence of VC is that 70% chance a fund performs above median if prior fund is 1st Q). However, more than ever, track record can be a very weak indicator if the fund is within <5-7 years.

    • Spread of how VCs are valuing the same companies is large.
    • Current TVPI to final DPI delta will be large for many funds, and some funds have resilient companies; others are filled w/companies that were pure momentum (but still marked up).
    Samir Kaji, Allocate

    There is an obvious desire from both sides to find something to show. As Luke put it, “we can pretend it’s all about independent thinking, non consensus and right, etc, but when you’re going out for Fund 2 and on a stack of unrealized, LPs want other signals.”

    This is why we end up focusing on ‘logo hunting’ and co-investment culture. If we’re all a gang, and we back each other up, then we’ll maintain the confidence of LPs. Meanwhile, the LPs are probably feeling a degree of comfort from investing in a few different funds, without realizing how intermingled and codependent they are.

    As Chamath Palihapitiya wrote in Advice to Startup Founders and Employees: Strength Doesn’t Always Come in Numbers:

    As it turns out, what VCs of the past decade assumed to be market alpha may have actually been market beta (i.e. fellow venture funds bidding up the same cohort of companies over several funding rounds).

    Chamath Palihapitiya, Social Capital

    This is clearly an undesirable outcome for LPs: The data for measuring venture capital fund performance is flimsy and creates a huge perverse incentives for GPs. This is clearly not good enough when so much capital is at stake. Especially when it involves pensions funds and university endowments. It’s a bad look for everyone.

    The final nail in the coffin here is how current practices can create a reality-distortion field around actual performance: in effect, a company’s ‘public’ valuation only changes when they want it to. This was outlined at length in a thread from Anand Sanwal of CBInsights, which included this slide from SVB:

    This is on the mind of every LP at the moment. What do their ‘paper’ returns from 2021/22 actually mean anymore? What will happen when the companies they are invested in via VC are forced to come to terms with reality?

    Meaningful benchmarks

    When you start talking about standardising anything in venture capital, there’s a reliably cold response. Everybody likes to believe they have their secret sauce, their intuition, their process, their edge over others… despite all signs pointing towards none of that changing the outcome.

    When you talk about measuring the performance of early stage companies, that’s when the real pushback begins. There’s too much uncertainty. It’s too unreliable. Projections are always a pipe-dream.

    There’s one simple response to these concerns: “Perfect is the enemy of good“.

    If you open yourself to new ways of looking at valuation (it’s not just about “market passing”), and new ways of performing valuation, you will find that there are practical, systematic frameworks to measure and report the development of private companies.

    Don’t get twisted up about producing an “accurate” result for an early stage company, it is foolishness – and not the point. The goal is to provide solid, useful benchmarks which can be calibrated against the market in a transparent manner.

    For an example of how this might be achieved, I will always recommend a read through Equidam’s methodology. It combines perspectives on verifiable characteristics via the qualitative methods, the exit potential via the VC method, and the vision for growth via the DCF methods. All packaged up into a nice, comprehensive report.

    What standardized reporting does for the LP/VC relationship

    If you can imagine a world where VCs produce quarterly reports on fund performance using a standardised framework, there are a number of profound benefits:

    1. LPs could better assess the performance of their existing VCs, creating more of a meritocracy.
    2. VCs would have an easier time raising, in addition to shortening their own internal feedback-loops to improve decision making.
    3. Moving away from current lazy valuation practices (ARR multiples) would help avoid extreme fluctuations in valuation, as we’ve experienced since 2021.
    4. It will (slowly) kill the dinosaurs, the giant firms which played a part in the development of this ecosystem and all of its flaws.
    5. A move towards transparency – especially around valuation – would be timely, as the SEC’s gaze falls on venture capital.
    6. There are also interesting considerations for liquidity in secondary markets serving private company equity, but that’s a whole post of its own.

    Conclusion

    It seems clear to me that this change will not come easily to venture capitalists, who are either comfortable with the status-quo or simply find it convenient. However, it might be possible for LPs to set new terms as market dynamics have shifted power in their direction.

    Still, this is a difficult argument to make. I’m suggesting no less than upending how much of venture capital operates, and I’m doing so from the position of a relative outsider.

    But I guess that’s the point? Venture capital has been a closed ecosystem for too long, full of esoteric practices shaped by a relatively tiny group of individuals. There is plenty of room for improvement, especially if we stop getting hung up on the need for ‘perfect’, when the current status is ‘poor’.

    Finally, a bigger point than any of the six I mentioned previously: if this makes us better at allocating capital to innovative ideas, and innovative people, then it’s got to be worthwhile.

  • Why venture capital should be consensus-averse

    Why venture capital should be consensus-averse

    In The General Theory of Employment, Interest and Money, Keynes wrote about investment through the metaphor of a newspaper contest to select the six best looking people from a group of photos, with the prize being awarded to the contestant whose choice most closely corresponded to the average of all contestants.

    Keynes’ point was that, despite the clear and simple instruction, contestants are actually not inclined to consider which of the photographed people are the best looking. Rather, they now consider a third-degree perspective of ‘what would the average person imagine that the average opinion is?’

    We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practise the fourth, fifth and higher degrees.

    John Maynard Keynes, Economist

    In A Simple Model of Herd Behavior, Abhijit V. Banerjee examined the inefficiencies created when decision making becomes reliant on signals from others. We become inclined to abandon our own data, in favor of prioritizing signals which themselves may also be based on nothing more than another prior signal. 

    If decreasing returns (average payoffs decline as the number of people who choose it increases) tends to reduce herding, one would expect increasing returns, which rewards doing what a lot of others are doing, to increase the tendency to herd. This is indeed what we find.

    Abhijit V. Banerjee, Ford Foundation International Professor of Economics at Massachusetts Institute of Technology

    There are a number of social psychological drivers of this behavior, but the most obvious are our desire to associate with popular choices, and the greater dispersion of responsibility if that choice proves wrong. 

    Consensus threatens innovation

    Generally, herd behavior is problematic in how it undermines sound judgment and rational choice, though by nature it tends to be low-stakes and risk-controlled. For venture capital, this innately human behavior should be viewed as an existential threat, running contrary to the needs of effectively identifying and funding innovation. 

    If no great book or symphony was ever written by committee, no great portfolio has ever been selected by one, either.

    Peter Lynch, former manager of the Magellan Fund at Fidelity Investments

    The root of the name venture capital, as Evan Armstrong reminds us in Venture Capital is Ripe for Disruption, is adventure capital. It’s only really an adventure if you’re not sure of the destination, and backing innovation is exactly that: you are straying into the unknown; high risk, large potential reward. 

    The classic archetype of a venture capitalist, fitting with this concept, is a highly perceptive and analytical individual who can evaluate all kinds of oddball, out-of-the-box startups and identify the ones with potential. Someone who sees opportunities where others do not, who does not care about (or actively avoids) pattern-matching with past successes, and who ignores the noise of signals from their peers.  

    There is an old saying in enterprise software, “No one is fired for buying IBM”—people mitigate risk for their decisions by choosing the consensus option.

    This occurs even in the supposedly risky world of venture capital.

    Evan Armstrong, ‘Reformed’ Venture Capitalist

    Hunger drives herd behavior

    In recent years, as the appetite for cheap capital grew to unsustainable heights, venture capitalists became preoccupied with following external signals to ascertain whether the market would agree to provide capital to their portfolio. Would their peers validate their investment choices? Would prospective LPs recognise the value of earlier investments if they weren’t shared with other respected names? Herd behavior crept in with pernicious effect; the seductive comfort of piling into seemingly safe deals with other investors. Manufacturing winners. 

    As long as downstream investors continued participating in the game of artificial value growth (and why wouldn’t they) it was still a good model, right?

    As long as the (paper) returns were good, it was still venture capital, right?

    We know how that ended. We also broadly know why it ended (crude valuation practices, interest rates making capital more expensive, exit markets rejecting inflated prices… etc). The question we should ask now is what can be done to stop it happening again? 

    Learning from mistakes

    Anyone involved in investment of any kind should be aware of the way signals should be handled (with oven gloves). It is valuable input that can shape an investment decision but shouldn’t drive it. For venture capital, that might mean reevaluating everything from deal flow management to valuation practices. 

    • Are the majority of your deals sourced through referrals from other investors?
    • When evaluating potential investments, how dependent is your conviction on recent similar deals? 
    • How much analytical rigor are you applying to the individual nature of each opportunity?
    • When setting valuation, how much do you rely on crude ARR multiples?
    • How much does the VC Twitter echo-chamber shape your approach to early stage investment, generally? 

    These might seem like basic questions, but there is clear cause to begin a first-principles reevaluation of how capital is allocated to ideas and founders. The responsibility is to effectively fund technological progress, not to exploit an uncertain market for short-term gains.

    A new approach, with a more analytical focus on individual businesses, may seem unrealistic: too much time involved, too much uncertainty. To that, I’ll close on three points:

    • Startups in 2023 are running leaner. The great hunger for capital is over, for now. That opens the opportunity to strike out and make fund returning deals without needing to drag other investors along with you. Your ability to identify winners (not simply agree on them) matters more than ever.
    • There are tools and frameworks which make analysing startups in detail much more practical (Equidam is an obvious example). Build a process which lets you collect data about opportunities and decisions, allowing you to develop and codify your experience.
    • Reconsider industry dogma about practices and perceptions (for example: about financial projections at early stage). More data = better decisions, you just need to pick the right lens to derive the right value.

    As many have said, the 2023 vintage has great promise. Particularly for investors who best adapt to the new conditions.

    [EDIT 26/03/2023: Adding a link to Chamath Palihapitiya’s article about herd behavior in venture funds and the risks involved. It’s a much more analytical perspective, which you can read here.]

    [EDIT 22/06/2025: Adding an overdue link to Geri Kirilova’s article about enmeshment in venture capital, providing another perspective on this problem, which you can read here.]