Early Stage vs Late Stage: Assessment, Prediction & Fluidity In The Equity Chain

Willy Braun
Jul 2 · 12 min read
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Photo by Luke Chesser on Unsplash
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https://blog.dealroom.co/wp-content/uploads/2018/11/The-Journey-to-Series-A-in-Europe.pdf
  • the market for exit needs to be large and dynamic enough to allow late stage investors to take the risks of funding high priced scale-ups (with significant goodwill), which is one of the reason why the chain of financing get stuck during crisis, when exits are frozen,
  • early stage investors have the luxury of not asking for precise models such as a proper business plan or market sizing because there activities are grounded on uncertainty, high failures rates and colossal payoff potentials (it’s both a luxury and a curse, depending on how you see things),
  • increasing valuation overtime leads late stage investors to look not only for big enough upside potential but also for more clarity about these payoff potentials and the attached risks involved. Thus, they will ask for more data about a company (the inside view), spend more time analyzing the market (economic -demand and competition- and financial — comparable equity stories and exit events — the outside view) and build more models (to balance properly the inside and the outside views),
  • these dynamics make it essential for founders to change drastically their fundraising materials and overall approach — you can’t ask late stage investors to make blind leap of faiths, it just does not work at their stage,
  • founders of startups that do not match certain patterns get frustrated because they cannot easily finance their ventures but the explanation lies largely on the lack of distributional information (and even early stage investors which does not formally use the outside view will get concerns about the magnitude of the potential payoff, which is a way of saying that they are not sure that they can keep their generic range of exit potential used, since the reference class seems too different for a legitimate use of groping),
  • during a crisis, investors need to get new distributional data to make good decisions: companies do not uniformly react so you need both to reassess all your references classes and how all classes are impacted,
  • to get a fully functioning equity chain, investors need to understand each other constraints and appropriate practices, and collaborate on the complementarities: early stage investors can share their perspectives on how the team works and how well they resolve issues (inside view), on what struck them compared to other teams in that stage, and their intuition on changes about technologies, consumer behaviors and products (let’s call that proto outside view), while trying to anticipate later stage frameworks and requirements (to select beforehand the fittest startups to match their investment criteria and to help the startups get there and prepare the adequate materials); later stage investors can share their perspectives on the outside view (both at the company level and about the macro-environment, both economic and financial), while trying to benefit from insights from earlier stage investors to fine-tune their hypothesis and best calibrate the residual need for intuition, since every tech companies, however mature, are facing certain degree of incertitude. This is by the way, one of the strengths of our positioning at Gaia Capital Partners, getting the perspectives from all early stage, growth and public market investors.
  • every decision relies on intuitive judgements, and they are often biased. The good news is that “intuitive judgements are often biased in a predictable manner“ (ibid). Hence, the problem is not wether to accept intuitive predictions at face value or reject them, but rather how they can be de-biaised and improved.“ And as you can guess it’s all about balancing properly singular and distributional information; the typical mistake extensively studied by Kahneman being the neglect of the outside view.
  1. Assessing the distribution for the reference class: Sometimes it’s an easy task — the statistics are available (and it’s tempting — and sometimes relevant — to modify the reference class to benefit from existing data). But more often you have to estimate the distribution on the basis of various sources of information.
  2. Making an intuitive estimation: It’s the activity that rely on leveraging the singular information about the particular case at hand, which distinguishes it from the other member of the reference class. This is the non-regressive step, done intuitively. It’s what many seed investors do exclusively. The next two steps are designed to improve this estimate.
  3. Assessing the predictability: It’s the step where you try to see if the type of information available in this case permits accurate prediction of outcomes. Focusing on situations where linear regressions are not possible (ie: when records of past prediction and outcomes does not exist), there are two main possibilities: 4.1. comparing the predictability of the variable concerned to predictability of other variables, eg: it’s less predictable than temperature but more predictable than stock prices. 4.2. asking questions such as: “if you were to consider two startups that you are about to fund, how often would you be right in predicting which of the two will generate a better return of investment?“ It should be noted that estimates of predictability are not easy to make — people are subject to hindsight fallacy, which leads to an overestimate of the predictability of outcomes, and to an availability bias, which leads them overly weight recent or memorable information.
  4. Correcting of the intuitive estimate: “To correct for nonregressiveness, the intuitive estimate should be adjusted toward the average of the reference class“. For the more curious readers, it would mean that the distance between the intuitive estimate and the average of the class should be reduced by a correlation coefficient. Taking an example, the concrete method would be the following: the investor prediction of the exit value is 200m€ and, on average, the exit value of companies who benefit from venture capital money is 100m€. Suppose that the investor believes that he would correctly orders pairs of startups by their future exit value on 75% of comparison. In this case, the predictive accuracy (a) can be estimated using the correlation coefficient (p), using the formula a = 2p — 1, in this case, a = 2*75% — 1 . = 0.5, and the regressed estimate of exit value would be 100 + 0.5(200–100) = 150m€
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Gaia Voice

Gaia Voice, as its name suggests, echoes the voices of Gaia Capital Partners team members. It’s a pl

Willy Braun

Written by

Operating Partner at @gaiacapital. Co-founder @daphnivc. Teacher (innovation & marketing). Author Internet Marketing 2013. I love books, ties and data.

Gaia Voice

Gaia Voice, as its name suggests, echoes the voices of Gaia Capital Partners team members. It’s a place to share our perspectives and analyses and lift the veil on our job as growth investors.

Willy Braun

Written by

Operating Partner at @gaiacapital. Co-founder @daphnivc. Teacher (innovation & marketing). Author Internet Marketing 2013. I love books, ties and data.

Gaia Voice

Gaia Voice, as its name suggests, echoes the voices of Gaia Capital Partners team members. It’s a place to share our perspectives and analyses and lift the veil on our job as growth investors.

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