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The Science of Re-Upping

baseball, follow on

Soooooooo… (I know, what a great word to start a blogpost) I started this essay, with some familiarity on one subject. Little did I know I was going to learn about an entirely different industry, and be endlessly fascinated about that.

The analogy that kicked off this essay is that re-upping on a portfolio company is very much like re-signing a current player on a sports team. That was it. Simple as it was supposed to sound. The goal of any analogy was to frame a new or nuanced concept, in this case, the science of re-upping, under an umbrella of knowledge we were already familiar with.

But, I soon learned of the complexity behind re-upping players’ contracts, as one might assume. And while I will claim no authority over the knowledge and calculations that go into contracts in the sports arena, I want to thank Brian Anderson and everyone else who’s got more miles on their odometer in the world of professional sports for lending me their brains. Thank you!

As well as Arkady Kulik, Dave McClure, and all the LPs and GPs for their patience and willingness to go through all the revisions of this blogpost!

While this was a team effort here, many of this blogpost’s contributors chose to stay off the record.

The year was 1997.

Nomar Garciaparra was an instantaneous star, after batting an amazing .306/.342/.534. For the uninitiated, those are phenomenal stats. On top of batting 30 home runs and 11 triples – the latter of which was a cut above the rest of the league, it won him Rookie of the Year. And those numbers only trended upwards in the years after, especially in 1999 and 2000. Garciaparra became the hope for so many fans to end the curse of the Bambino – a curse that started when the Red Sox traded the legendary Babe Ruth to the Yankees in 1918.

Then 2001 hit. A wrist injury. An injured Achilles tendon. And the fact he needed to miss “significant time” earned him a prime spot to be traded. Garciaparra was still a phenomenal hitter when he was on, but there was one other variable that led to the Garciaparra trade. To Theo Epstein, above all else, that was his “fatal flaw.”

Someone that endlessly draws my fascination is Theo Epstein. Someone that comes from the world of baseball. A sport that venture draws a lot of inspiration, at least in analogy, like one of my fav sayings, Venture is one of the only types of investments where it’s not about the batting average but about the magnitude of the home runs you hit.

If you don’t follow baseball, Theo Epstein is the youngest general manager in the history of major league baseball at 26. But better known for ending the Curse of the Bambino, an 86-year curse that led the Red Sox down a championship drought that started when the Red Sox traded Babe Ruth to the Yankees. Theo as soon as he became general manager traded Nomar Garciaparra, a 5-time All-star shortstop, to the Cubs, and won key contracts with both third baseman Bill Mueller and pitcher Curt Schilling. All key decisions that led the Red Sox to eventually win the World Series 3 years later.

And when Theo left the Red Sox to join the Chicago Cubs, he also ended another curse – The Curse of the Billy Goat, ending with Theo leading them to a win in the 2016 World Series. You see, in baseball, they measure everything. From fly ball rates to hits per nine innings to pitches per plate appearance. Literally everything on the field.

But what made Theo different was that he looked at things off the field. It’s why he chose to bet on younger players than rely on the current all-stars. It’s why he measures how a teammate can help a team win in the dugout. And, it’s why he traded Nomar, a 5-time All Star, as soon as he joined, because Nomar’s “fatal flaw” was despite his prowess, held deep resentment to his own team, the Sox, when they tried to trade him just the year prior for Alex Rodriguez but failed to.

So, when Danny Meyer, best known for his success with Shake Shack, asked Theo what Danny called a “stupid question”, after the Cubs lost to the Dodgers in the playoffs, and right after Houston was hit by a massive hurricane, “Theo, who are you rooting for? The Dodgers so you can say you lost to the winning team, or Houston (Astros), because you want something good to happen to a city that was recently ravaged by a hurricane.”

Theo said, “Neither. But I’m rooting for the Dodgers because if they win, they’ll do whatever every championship team does and not work on the things they need to work on during the off season. And the good news is that we have to play them 8 times in the next season.”

You see, everyone in VC largely has access to the same data. The same Pitchbook and Crunchbase stat sheet. The same cap table. And the same financials. But as Howard Marks once said in response how you gain a knowledge advantage:

“You have to either:

  1. Somehow do a better job of massaging the current data, which is challenging; or you have to
  2. Be better at making qualitative judgments; or you have to
  3. Be better at figuring out what the future holds.”

For the purpose of this blogpost, we’re going to focus on the first one of the three.

What is value?

To begin, we have to first define a term that’ll be booking its frequent flier miles for the rest of this piece – expected value.

Some defined it as the expectation of future worth. Others, a prediction of future utility. Investopedia defines it as the long-term average value of a variable. Merriam-Webster has the most rudimentary definition:

The sum of the values of a random variable with each value multiplied by its probability of occurrence

On the other hand, venture is an industry where the beta is arguably one of the highest. The risk associated with outperformance is massive as well. And the greatest returns, in following the power law, are unpredictable.

We’re often blessed with hindsight bias, but every early-stage investor in foresight struggles with predicting outlier performance. Any investor that says otherwise is either deluding you or themselves or both. At the same time, that’s what makes modeling exercises so difficult in venture, unlike our friends in hedge funds and private equity. Even the best severely underestimate the outcomes of their best performers. For instance, Bessemer thought the best possible outcome for Shopify was $400M with only a 3% chance of occurring.

Similarly, who would have thought that jumping in a stranger’s car or home, or live streaming gameplay would become as big as they are today. As Strauss Zelnick recently said, “The biggest hits are by their nature, unexpected, which means you can’t organize around them with AI.” Take the word AI out, and the sentence is equally as profound replaced with the word “model.” And it is equally echoed by others. Chris Paik at Pace has made it his mission to “invest in companies that can’t be described in a single sentence.”

But I digress.

Value itself is a huge topic – a juggernaut of a topic – and I, in no illusion, find myself explaining it in a short blogpost, but that of which I plan to spend the next couple of months, if not years, digging deeper into, including a couple more blogposts that are in the blast furnace right now. But for the purpose of this one, I’ll triangulate on one subset of it – future value as a function of probability and market benchmarks.

In other words, doubling down. Or re-upping.

For the world of startups, the best way to explain that is through a formula:

E(v) = (probability of outcome) X (outcome)

E(v) = (graduation rate) X (valuation step up from last round) X (dilution)

For the sake of this blogpost and model, let’s call E(v), appreciation value. So, let’s break down each of the variables.

Graduation rates

What percent of your companies graduate to the next round? I shared general benchmarks in this blogpost, but the truth is it’s a bit more nuanced. Each vertical, each sub-vertical, each vintage – they all look different. Additionally, Sapphire’s Beezer recently said that it’s normal to expect a 20-30% loss ratio in the first five years of your fund. Not all your companies will make it, but that’s the game we play.

On a similar note, institutional LPs often plan to build a multi-fund, multi-decade relationship with their GPs. If they invest in a Fund I, they also expect to be there by Fund III.

Valuation step ups

How much greater is the next round’s valuation in comparison to the one in which you invested? Twice as high? Thrice? By definition, if you double down on the same company, rather than allocate to a net new company, you’re decreasing your TVPI. And as valuations grow, the cost of doubling down may be too much for your portfolio construction model to handle, especially if you’re a smaller sub-$100M fund.

It’s for the same reason that in the world of professional sports, there are salary caps. In fact, most leagues have them. And only the teams who:

  • Have a real chance at the championship title.
  • Have a lot in their coffers. This comes down to the composition of the ownership group, and their willingness to pay that tax.
  • And/or have a city who’s willing to pay the premium.

… can pay the luxury tax. Not to be too much of a homer, but the Golden State Warriors have a phenomenal team and are well-positioned to win again (at least at the time of this blogpost going out). So the Warriors can afford to pay the luxury tax, but smaller teams or teams focused on rebuilding can’t.

The Bulls didn’t re-sign the legendary Michael Jordan because they needed to rebuild. Indianapolis didn’t extend Peyton Manning’s contract ‘cause they didn’t have the team that would support Peyton’s talents. So, they needed to rebuild with a new cast of players.

Similarly, Sequoia and a16z might be able to afford to pay the “luxury tax” when betting on the world’s greatest AI talent and for them to acquire the best generative AI talent. Those who have a real chance to grow to $100M ARR, given adoption rates, retention rates, and customer demand. But as a smaller fund or a fund that has a new cast of GPs (where the old guard retired)… can you?

Dilution

If a star player is prone to injury or can only play 60 minutes of a game (rather than 90 minutes), a team needs to re-evaluate the value of said player, no matter how talented they are. How much of a player’s health, motivation, and/or collaborativeness – harkening back to the anecdote of Nomar Garciaparra at the beginning – will affect their ability to perform in the coming season?

Take, for instance, the durability of a player. If there ‘s a 60% chance of a player getting injured if he/she plays longer than 60 minutes in a game and a 50% of tearing their ACL, while they may your highest scorer this season, they’re not very durable. If that player missed 25% of practices and 30% of games, they just don’t have it in them to see the season through. And you can also benchmark that player against the rest of the team. How’s that compared with the team’s average?

Of course, there’s a parallel here to also say, every decision you make should be relative to industry and portfolio benchmarks.

How great of a percentage are you getting diluted with the next round if you don’t maintain your ownership? This is the true value of your stake in the company as the company grows.

How does one use the appreciation value equation?

E(v) = (graduation rate) X (valuation step up from last round) X (dilution)

If the expected value is greater than one, the company is probably not worth re-upping. And that probably means the company is overhyped, or that that market is seeing extremely deflated loss ratios. In other words, more companies than should be, are graduating to the next stage; when in reality, the market is either a winner-take-all or a few-take-all market. If it is less than or equal to one, then it’s ripe to double down on. In other words, the company may be undervalued.

And to understand the above equation or for it to be actually useful (outside of an abstract concept), you need market data. Specifically, around valuation step ups as a function of industry and vertical.

If you happen to have internal data across decades and hundreds of companies, then it’s worth plugging in your own dataset as well. It’s the closest you can get to the efficient market frontier.

But if you lack a large enough sample size, I’d recommend the below model constructed from data pulled from Carta, Pitchbook, and Preqin and came from the minds of Arkady Kulik and Dave McClure.

The model

The purpose of this model is to help your team filter what portfolio companies are worth diving deeper into and which ones you may not have to (because they didn’t pass the litmus test) BEFORE you evaluate additional growth metrics.

It is also important to note that the data we’ve used is bucketed by industry. And in doing so, assumptions were made in broad strokes. For example, deep tech is broad by design but includes niche-er markets that have their own fair share of pricing nuances in battery or longevity biotech or energy or AI/ML. Or B2B which include subsectors in cybersecurity or infrastructure or PLG growth.

Take for instance…

Energy

Energy sector appreciation values and follow-on recommendations

The energy sector sees a large drop in appreciation value at the seed stage, where all three factors contribute to such an output. Valuation step-up is just 1.71X, graduation rates are less than 50% and dilution is 38% on average.  

Second phase where re-upping might be a good idea is Series B. Main drivers as to such a decision are that dilution hovers around 35% and about 50% of companies graduate from Series A to Series B. Mark ups are less significant where we generally see only an increase in valuation at about 2.5X, which sits around the middle of the pack.

Biotech

Biotech sector appreciation values and follow-on recommendations

The biotech sector sees a large drop in appreciation value at the Seed stage. This time, whereas dilution seems to match the pace of the rest of the pack (at an average of 25%), the two other factors shine greater in making a follow-on decision. Valuation step up are rather low, sitting at 1.5X. And less than 50% graduate to the next stage.

In the late 2023 market, one might also consider re-upping at the Series C round. Main driver is the unexpectedly low step-up function of 1.5X, which matches the slow pace of deployment for growth and late stage VCs. On the flip side, a dilution of 17% and graduation rate of 60% are quite the norm at this stage.

In closing

All in all, the same exercise is useful in evaluating two scenarios – either as an LP or as a GP:

  1. Is your entry point a good entry point?
  2. Between two stages, where should you deploy more capital?

For the former, too often, emerging GPs take the stance of the earlier, the better. Almost as if it’s a biblical line. It’s not. Or at least not always, as a blanket statement. The point of the above exercise is also to evaluate, what is the average value of a company if you were to jump in at the pre-seed? Do enough graduate and at a high enough price for it to make sense? While earlier may be true for many industries, it isn’t true for all, and the model above can serve as your litmus test for it. You may be better off entering at a stage with a higher scoring entry point.

For the latter, this is where the discussion of follow on strategies and if you should have reserves come into play. If you’re a seed stage firm, say for biotech, using the above example, by the A, your asset might have appreciated too much for you to double down. In that case, as a fund manager, you may not need to deploy reserves into the current market. Or you may not need as large of a reserve pool as you might suspect. It’s for this reason that many fund managers often underallocate because they overestimate how much in reserves they need.

If you’re curious to play around with the model yourself, ping Arkady at ak@rpv.global, and you can mention you found out about it through here. 😉

Photo by Gene Gallin on Unsplash

Stay up to date with the weekly cup of cognitive adventures inside venture capital and startups, as well as cataloging the history of tomorrow through the bookmarks of yesterday!

The views expressed on this blogpost are for informational purposes only. None of the views expressed herein constitute legal, investment, business, or tax advice. Any allusions or references to funds or companies are for illustrative purposes only, and should not be relied upon as investment recommendations. Consult a professional investment advisor prior to making any investment decisions.

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The Science of Selling – Early DPI Benchmarks

The snapshot

Some of you reading here are busy, so we’ll keep this top part brief, as an abstract sharing our top three observations of leading fund managers.

Generally speaking, don’t sell your fast growing winners early.

Except when…

Selling on your way up may not be a crazy idea.

  1. You might sell when you want to lock in DPI. Don’t sell more than 20% of your fund’s positions unless you are locking in meaningful DPI for your fund. For instance, at each point in time, something that’s greater than 0.5X, 1X, 2X, or 3X of your fund size.
  2. You might consider selling when you’ve lost conviction. Consider selling a position when you feel the market has over-priced the actual value, or even up to 100% if you’ve lost conviction.
  3. You might consider selling when one is growing slower than your target IRR. If companies are growing slower and even only as fast as your target IRR, consider selling if not at too much of a discount (Note: there may be some political and/or signaling issues to consider here as well. But will save the topic of signaling for another blog post).

Do note that the above are not hard and fast rules. Every decision should be made in context to other moving variables. And that the numbers below are tailored to early-stage funds.

Net TVPI Benchmarks from Years 5-15
Net DPI Benchmarks from Years 5-15

Let’s go deeper…

On a cloudless Friday morning, basking in the morning glory of Los Altos, between lattes and croissants, between two nerds (or one of whom might identify as a geek more than a nerd), we pondered one question:

How much of selling is art? How much is science?

Between USV selling 30% of their Twitter stake, Menlo selling half of their Uber, Benchmark only selling 15% of their Uber pre-IPO shares, and Blackbird recently selling 20% of its Canva stake, it feels more like the former than the latter. Then when Howard Marks says selling is all about relative selection and the opportunity cost of not doing so, it seems to reinforce the artistic form of getting “moolah in da coolah” to borrow a Chris Douvos trademark.

Everyone seems to have a financial model for when and how to invest, but part of being a fiduciary of capital is also knowing when to distribute – when to sell. When RVPI turns into DPI. And we haven’t seen many models for selling yet. At least none have surfaced publicly or privately for us.
The best thought piece we’ve seen in the space has been Fred Wilson’s Taking Money “Off the Table”. At USV, they “typically seek to liquidate somewhere between 10% and 30% of our position in these pre-IPO liquidity transactions. Doing so allows us to hold onto the balance while de-risking the entire investment.”

Source: Fred Wilson’s Taking Money “Off The Table”

In aggregate, we’ve seen venture fund distributions follow very much of the power law – whether you’re looking at Correlation’s recent findings…

Source: Correlation Ventures

Or what James Heath has found across 1000+ firms’ data on Pitchbook.

Source: James Heath

As such, it gave birth to a thought… What if selling was more of a science?

What would that look like?

Between two Daves, it was not the Dave with sneakers and a baseball cap and with the profound disregard to healthy diets, given the fat slab of bacon in his croissan’wich, who had the answer there.

“To start off, in a concentrated portfolio of 30 investments, a fund returner is a 30x investment. For a 50-investment fund, it’s 50x. And while hitting the 0.5x DPI milestone by years 5-8, and a 2x DPI milestone by years 8-12, is the sign of a great fund, you shouldn’t think about selling much of your TVPI for DPI unless or until your TVPI is starting to exceed 2-3x.” Which seems to corroborate quite well with Chamath Palihapitiya’s findings that funds between 2010 and 2020 convert have, on average, converted about 25% of their TVPI to DPI.

“Moreover, usually you shouldn’t be selling more than 20% of the portfolio at one time (unless you’re locking in / have already locked in 3X or more DPI). You should be dollar-cost averaging – ensuring time diversity – on the way out as well. AND usually only if a company that’s UNDER-growing or OVER-valued compared to the rest of your portfolio. Say your portfolio is growing at 30% year-over-year, but an individual asset is growing slower at only 10-20% OR you believe it is overvalued, that’s when you think about taking cash off the table. Sell part (or even all) of your stake, if selling returns a meaningful DPI for the fund, and if you’re not capping too upside in exchange for locking in a floor.”

Meaningful DPI, admittedly, does mean different benchmarks for different kinds of LPs. For some, that may mean 0.25X. For others that may mean north of 0.5X or 1X.

“On the other hand, if a company is outperforming / outgrowing the rest of the portfolio, generally hold on to it and don’t sell more than 10-20% (again, unless you’re locking in meaningful DPI, or perhaps if it’s so large that it has become a concentration risk).”

I will caveat that there is great merit in its counterpart as well. Selling early is by definition capping your upside. If you believe an asset is reaching its terminal value, that’s fine, but do be aware of signaling risk as well. The latter may end up being an unintended, but self-fulfilling prophecy.

So, it begged the question: Under the assumption that funds are 15-year funds, what is meaningful DPI? TVPI? At the 5-year mark? 7.5 years in? 10 years? And 12.5 years?

The truth is the only opportunities to sell come from the best companies in your portfolio. And probably the companies, if anything, you should be holding on to. By selling early, you are capping your downside, but at the same time capping your upside on the entire portfolio. When the opportunity arises to lock in some DPI, it’s worth considering the top 3-5 positions in your fund. For instance, if your #2 company is growing quickly, you may not be capping the upside as much.

Do keep in mind that sometimes it’s hard to fully conceptualize the value of compounding. As one of my favorite LPs reminded me, if an asset is growing 35% year-over-year, the last 20% of the time produces 56% of the return. Or if an asset is growing 25% YoY, if you sell 20% earlier (assuming 12 year time horizons), you’re missing out on 45% of the upside.

As a GP, you need to figure out if you’re IRR or multiple focused. Locking in early DPI means your IRR will look great, but your overall fund multiple may suffer.

As an LP, that also means if the gains are taxable (meaning they don’t qualify for QSBS or are sold before QSBS kick in), you need to pay taxes AND find another asset that’s compounding at a similar or better rate. As Howard Marks puts it, you need to find another investment with “superior risk-adjusted prospective returns.”

And so began the search for not just moolah in da coolah, but how much moolah in da coolah is good moolah in da coolah? And how much is great?

Net TVPI Benchmarks from Years 5-15
Net DPI Benchmarks from Years 5-15

Some caveats

Of course, if you’ve been around the block for a minute, you know that no numbers can be held in isolation to others. No facts, no data points alienated from the rest.

Some reasons why early DPI may not hold as much weight:

  • Early acqui-hires. Usually not a meaningful DPI and a small, small fraction of the fund.
    • There’s a possibility this may be the case for some 2020-2021 vintages, as a meaningful proportion of their portfolio companies exit small but early.
    • In other words, DPI is constructed of small, but many exits, rather than a meaningful few exits.
  • TVPI is less than 2-3x of DPI, only a few years into the fund. In other words, their overall portfolio may not be doing too hot. Obviously, the later the fund is to its term, the more TVPI and DPI are alike.
  • As a believer in the power law, if on average it takes an outlier 8 years to emerge AND the small percentage of winners in the portfolio drive your return, your DPI will look dramatically different in year 5 versus 10. For pre-seed and seed funds, it’s fair to assume half (or more) companies go to zero within the first 3-5 years. And in 10 years, more than 80% of your portfolio value comes from less than 20% of your companies. Hell, it might even be 90% of your portfolio value comes from 10% of your companies. In other words, the power law.
  • GPs invested in good quality businesses. Some businesses may not receive markups, but may be profitable already, or growing consistently year-over-year that they don’t need to raise another round any time soon.
  • Additionally, if you haven’t been in the investing game for long, persistence of track record, duration, and TVPI may matter more in your pitch. If you’ve been around the block, IRR and DPI will matter more.
  • As the great Charlie Munger once said, “selling for market-timing purposes actually gives an investor two ways to be wrong: the decline may or may not occur, and if it does, you’ll have to figure out when the time is right to go back in.” For private market investors, unless you can buy secondaries, you’ll never have a time to go back in until the public offering. As such, it is a one-way door decision.

Some LPs are going to boast better portfolios, and we do admit there will be a few with portfolios better than the above “benchmarks.” And if so, that’s a reason to be proud. In terms of weighting, as a proponent of the power law, there is a high likelihood that we’ve underestimated the percent of crap and meh investments, and overestimated the percent of great investments in an LP’s portfolio. That said, that does leave room for epic fund investments that are outliers by definition. 

We do admit that, really, any attempt to create a reference point for fund data before results speak for themselves is going to be met with disagreement. But we also understand that it is in the discourse, will we find ourselves inching closer to something that will help us sleep better at night.

One more caveat for angels… The truth is as an angel, none of the above really matter all that much. You’re not a fiduciary of anyone else’s capital. And your time horizons most likely look different than a fund’s. It’s all yours. So it’s not about capping your downside, but more so about capping your regret. In other words, a regret minimization framework (aka, “spouse regret/yelling minimization insurance”). 

That will be so unique to you that there is no amount of cajoling that we could do here to tell you otherwise. And that your liquidity timelines are only really constrained by your own liquidity demands.. For instance, buying a new home, sending kids to college, or taking care of your parents (or YOU!) in their old age.

But I do think the above is a useful exercise to think through selling if you had a fund. You would probably break it down more from a bottoms up perspective. What is your average check size? Do you plan to have a concentrated portfolio of sub-30 investments? Or more? Do you plan to follow on? How much if so? And that is your fund size.

In closing

Returning above a 3x DPI is tough. Don’t take our words for it. Even looking at the data, only 12.5% of funds return over a 3x DPI. And only 2.5% return three times their capital back on more than 2 separate funds.

In the power law game we play, as Michael Mauboussin once said, “A lesson inherent in any probabilistic exercise: the frequency of correctness does not matter; it is the magnitude of correctness that matters.” Most will return zero, or as Jake Kupperman points out: More than 50%.

Source: Jake Kupperman’s The Time Has Come to Modernize the Venture Capital Fund of Funds

But it’s in the outliers that return meaningful DPI, not the rest. Not the acqui-hire nor really that liquidation preference on that small acquisition.

At the end of the day, the goal isn’t for any of the above to be anyone’s Bible, but that it’d start a conversation about how people look at early returns. If there is any new data points that are brought up as a result of this blogpost, I’ll do my best to update this thread post-publication.

Big thank you to Dave McClure for inspiring and collaborating on this piece, and to Eric Woo and all our LP friends who’ve helped with the many revisions, sharing data, edits, language and more. Note: Many of our LP friends chose to stay anonymous but have been super helpful in putting this together.

Footnotes

For the purpose of this piece, we know that “good” and “great”, in fact all of the superlative adjectives, are amorphous goalposts. And those words may mean different things to different people. This blogpost isn’t meant to establish a universal truth, but rather serve as a useful reference point for both LPs, looking for “benchmarking” data, and GPs to know where they stand. For the latter, if your metrics do fall in the “good” to “great” range, they’re definitely worth bragging about.

And so with that long preamble, in the piece above, we defined “good” as top quartile, and “great” as top decile. “Good” as a number on its own, enough for an LP to engage in a conversation with you. And “great” as a number that’ll make LPs running to your doorstep. Or at least to the best of our portfolios, leveraging both publicly reported and polled numbers as well as our own.

Our numbers above are also our best attempt in predicting steady state returns, divorcing ourselves from the bull rush of the last 3-5 vintage years. As such, we understand there are some LPs that prefer to do vintage benchmarking, as opposed to steady state benchmarking. And this blogpost, while it has touched on it, did not focus on the former’s numbers.

EDIT (Aug 18, 2023): Have gotten a few questions about where’s the data coming from. The above numbers in the Net DPI and Net TVPI charts are benchmarks the LPs and I agreed on after looking into our own anecdotal portfolios (some spanning 20+ years of data), as well as referencing Cambridge data. These numbers are not the end-all-be-all, and your mileage as an LP may very much vary depending on your portfolio construction. But rather than be the Bible of DPI/TVPI metrics, the purpose of the above is give rough reference points (in reference to our own portfolios + public data) for those who don’t have any reference points.

Cover Photo by Renate Vanaga on Unsplash

Stay up to date with the weekly cup of cognitive adventures inside venture capital and startups, as well as cataloging the history of tomorrow through the bookmarks of yesterday!

The views expressed on this blogpost are for informational purposes only. None of the views expressed herein constitute legal, investment, business, or tax advice. Any allusions or references to funds or companies are for illustrative purposes only, and should not be relied upon as investment recommendations. Consult a professional investment advisor prior to making any investment decisions.

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An Investor’s Job is to Hear the Silence

headphones, earbuds, listening, hearing

In the world of venture, hell, even in the broader world of investments, we are blessed and cursed a cosmos of information. A data ocean, as some may call it. In the words of the great Howard Marks,

“You have to either:

  1. Somehow do a better job of massaging the current data, which is challenging; or you have to
  2. Be better at making qualitative judgments; or you have to
  3. Be better at figuring out what the future holds.”

And while I go in length why the above are true in a former piece… today, I want to postulate a fourth.

Be better at listening to the silence.

Let me elaborate.

Facts and opinions

If I were to ask you, what day’s your birthday? I bet you could answer pretty quickly.

And the same would be true, if I asked you the color of the sky. Or what you ate for breakfast in the morning. Questions on facts have factual answers. There is either one immediate answer, or an answer you know exactly how to find, and at the very end, still a definitive answer. An example of the latter would be, What is the temperature right now?

On the other hand, if I were to ask you, what do you think about your life partner? The answer varies. You might say she or he is reliable. Or caring. And kind. And if I follow up with silence, you might spend some time thinking and filling the void with more words. Those words… are powerful. They simmer all of your life experiences and your stories — all your trials and tribulations, years, months, weeks, days, hours and minutes — onto a neatly organized platter for the other person. Those words that summarize it all are powerful. But what’s even more interesting to investor is the time it takes to come up with those words. That precious time, as your life is playing out like a flipbook, spends its precious milliseconds hugging silence.

No matter how miniscule those gaps are, they exist. And our goal as investors, and even more so for startup investors or emerging fund investors, with very little data to go on, is to create new datasets. In essence, to ask questions where the answers don’t just fill the air with vibrations, but to find answers that are dotted with tranquil stillness.

Great investors read between the lines. Listen to the pauses — the spaces between words. They look for the quiet thing out loud.

That silence is often more telling than anything you could put on a pitch deck or in a templated answer of “Tell me about your company.”

In closing

I know in this side of the world, we talk a lot about 10-year overnight successes. But let’s focus on the first two words of that phrase first. Ten-year. Startup journeys are long. They’re arduous. More things will go wrong than right. In the words of a serial founder with a few 9-figure exits under his belt, he once told me, “This shit sucks.” It’s tough. And if anyone discounts that — be it founder, operator, investor, friend or family — they don’t get it.

But that’s the very reason why investors look for grit, passion, and for me, obsession. But it’s also not a question we can really ask without getting a gift-wrapped, carefully-prepared answer. And so pushing the boundaries of questions is our job as investors. Why? Because even if for a moment, it sheds light into who we’re truly talking to.

And if there’s evidence of grit, passion, or obsession there, there might be something special.

Photo by Adrian Regeci on Unsplash

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The views expressed on this blogpost are for informational purposes only. None of the views expressed herein constitute legal, investment, business, or tax advice. Any allusions or references to funds or companies are for illustrative purposes only, and should not be relied upon as investment recommendations. Consult a professional investment advisor prior to making any investment decisions.

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When Should You Sell Your Shares As An Investor?

options, comparison, relative selection, when to sell

Recently, I stumbled across a captivating perspective on aphorisms via Tim Ferriss’ 5-Bullet Fridays. The Procrustean Bed. To be fair, before reading it on Tim’s newlsetter, I haven’t even heard of the concept. In one of his newsletters, he cites two incredible sources:

” ‘Something designed to produce conformity by unnatural or violent means. In Greek mythology, Procrustes was a robber who tied his victims to a bed, either stretching or cutting off their legs in order to make them fit it.’ (Source: Oxford Dictionary of English Idioms).

“Nassim Taleb has a related book of aphorisms titled The Bed of Procrustes. He explains the title thusly: ‘Every aphorism here is about a Procrustean bed of sorts—we humans, facing limits of knowledge, and things we do not observe, the unseen and the unknown, resolve tension by squeezing life and the world into crisp commoditized ideas, reductive categories, specific vocabularies, and prepackaged narratives, which, on the occasion, has explosive consequences.’ “

Down the investing rabbithole

There exist a number of aphorisms in the investing world. Chief of which reads “buy low, sell high.” Public market assets are quite liquid. Hypothetically, you can cash out whenever you want. Such liquidity has paved way for psychological inconsistencies to maximize gratification. In language with unnecessary jargon redacted, the option to sell is less motivated by rational thinking but more by fear of losing money – loss aversion. If you invest $100 into the public market, you can choose if you want to cash out at $95, $90, or $120 or $200. While there is a non-zero chance of you losing your entire principal, chances are you’ll liquidate your positions before that happens.

On the other hand, private market investments are illiquid. Upon investment, there is no liquid market in which you can sell immediately. At best, you have to wait 3-5 years before a rapidly marked-up investment creates opportunities for distributions in the secondary market. In other words, cash money while companies are still private. In the private markets, your principal either appreciates in multiples, rather than percentages, or bottoms out. Any in-betweens will neither make or break your investment strategy, and are often out of your immediate control. So in this case, illiquidity is a feature, not a bug.

The notion of exiting positions as a private market investor, therefore, gravitates towards a singularity – when you make a damn good investment. The only time you really have an option to choose whether you can sell or not, when otherwise, it becomes a tax write-off or a small exit outside of your immediate control.

When should you sell?

Should you ever sell?

And if you sell, how much should you sell?

To answer all the above questions…

With the help of Shawn and Ratan, I wrote a blogpost on how to think about exiting positions at the beginning of this year. A topic of which I am still very much a rookie at, which may be quite apparent in this essay as well. Nevertheless I’m going to try to elaborate more on the notion of selling positions as an early-stage investor.

In a memo earlier this year, Howard Marks wrote that there are two main reasons people choose to sell: “because they’re up and because they’re down.”

When “they’re down”

Let’s start with the latter. When “they’re down.” Like I mentioned before, there are often very few options to sell when things are down. While I’m not proud that these investors exist in the early-stage private markets, I’ve seen and heard of some investors who try to make a last ditch effort to regain some of their principal when the startup goes south. Selling off IP. As well as assets. Or forcing the founders to make a modest exit, so that the investors cap their downside. Maybe at best, this returns them 2x on their capital (rarely the case).

But let’s say that’s the “best” case scenario. And let’s say it’s a $25M Fund I, writing $250K checks. A 2x net return means they got back $750K. $750K is far from returning the $25M fund. Not even close to doing so. You need over 30 of those “exits” to just break even for your fund. So, if you’re an investor penny pinching here, you’re in the wrong game AND you’re going to lose out on the relationships with the founding team.

Why the wrong game?

Venture is a hit-driven business. It’s not about your batting average but about the magnitude of the home runs you hit. We bat for 100x returns, which also increases the probability of misses, determined by ability to return the fund or not. If you’re optimizing for local maximums, you’d probably do better as a public market investor.

And why do relationships matter?

One, the startup world is a smaller world than you think. People gossip.

Two, statistically, first swings at bat rarely work out. In research done by Cowboy Ventures, they found 80% of unicorns had at least one co-founder with previous founding experience. Paris Innovation Review also found that “86% started their project with a partner, after having created other companies.” Two of many other studies. So, even though this venture didn’t achieve financial success for an investor, the next might. Or the one after that. Assuming you bet on the right people, it’ll just take a couple iterations before timing, market, and product also match up. If you leave on bad terms on this deal, you won’t be able to get in when things do work out.

Three, what makes early-stage investing incredible is the relationships you build along the way. The ability to learn and grow with really smart people.

When “they’re up”

The question of if to sell often leads to controversial debate. I know of some investors who never sell any of their stock. And that if they sell, to them, it is a measure of their lack of faith in a founder. And they would never want to feel that they’re betting against the founders. That’s okay if you’re an angel. But if you’re a VC, you have a fiduciary responsibility to your investors, which means you’ll eventually have to sell.

The question of when to sell is often answered in broad strokes with laws around QSBS, which states that if you hold a qualified small business stock for longer than five years, you’re not subject to capital gains taxes in the US. But should you sell in the 6th year or 10th year? And under what market conditions? Do you sell in a boom market or on the precipice of a bust market? For a company you believe in the long-term potential, regardless of short-term fluctuations, I’m a big fan of what Bill Miller said in his Q3 2021 Market Letter. “We believe time, not timing, is the key to building wealth in the [market].”

But when things are going really, really, really well, it’s okay to take money off the table, even ahead of the end of the fund’s 10-year lifespan. In fact, Union Square Ventures generally sells 15-30% of their position in their top portfolio companies to distribute back to their LPs. Fred Wilson‘s personal framework lies around “[selling] one third of the position immediately, put one third away for a long term hold, and actively manage the other third.”

To most, including myself, the goalposts for selling how much seem arbitrary. USV sold 30% of their position in Twitter to return twice the entire fund. Menlo Ventures sold almost half of their stake in Uber when Softbank offered to buy. Whereas, Benchmark sold 15% of its Uber shares. I also have really smart friends who liquidate 50% of their stake in a token if a single cryptocurrency reaches double digit percentages of their net worth.

It’s all about the opportunity cost

In a game where arbitrage matters, and the “why” matter more than the “what”, it was love at first sight when Howard Marks shared his mental model on selling. He boils it down to the simple economic concept of opportunity cost:

  1. “If your investment thesis seems less valid than it did previously and/or the probability that it will prove accurate has declined, selling some or all of the holding is probably appropriate.
  2. “Likewise, if another investment comes along that appears to have more promise – to offer a superior risk-adjusted prospective return – it’s reasonable to reduce or eliminate existing holdings to make room for it.”

In sum, the option to sell is not an isolated decision, but rather one which considers the other investment opportunities you have available to you. For a number of VCs, this breaks into the calculus of recycling carry and what to use early distributions to invest in next. If you’re a VC with consistent AND high-quality deal flow, you’d probably want to reinvest. If you’re a VC without either of the two (i.e. only consistency or quality) or an emerging angel, your goal is to get both. In having both, you then have access to relative selection.

Photo by Sina Asgari on Unsplash

Stay up to date with the weekly cup of cognitive adventures inside venture capital and startups, as well as cataloging the history of tomorrow through the bookmarks of yesterday!

Any views expressed on this blog are mine and mine alone. They are not a representation of values held by On Deck, DECODE, or any other entity I am or have been associated with. They are for informational and entertainment purposes only. None of this is legal or investment advice. Please do your own diligence before investing in startups and consult your own adviser before making any investments.

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