10%= correction, 20%= bear market.

( Source : https://theirrelevantinvestor.wordpress.com/2016/01/12/probably-everything-you-need-to-know-about-bear-markets/ )


10%= correction, 20%= bear market.

I know these arbitrary numbers sometimes seem silly, but when looking at the data, you have to draw the line somewhere. Get over it, let’s move on.

The S&P 500 has been in a drawdown for the last eight months. Stocks are currently 9.2% below the highs made in May 2015, just a hair away from official correction territory. Everyone knows this is totally normal, but you might be surprised to know that since 1928, stocks have been in a 10% drawdown 55% of the time. The problem of course is that they never feel normal because we don’t know in real time if this is just a correction or the start of a bear market. And the deeper stocks go, the harder it is to resist fear’s temptation.

In times like this, historical facts don’t provide much comfort and even less of a roadmap, however, hopefully they can provide a little context.

Since 1928, there have been fifteen separate drawdowns of 10%. Before I continue, you might be thinking, “only fifteen corrections, that doesn’t sound right.” Here’s how I look at drawdowns; in my mind, a drawdown is not over until new highs are made. Of these fifteen corrections, ten have turned into a bear market.

Let’s take a closer examination of these 20% declines because not all bear markets are created equal. There are secular bear markets, which by their nature can only be defined after the fact. These are long periods of time in which stocks make little progress. Then there are cyclical bears, which can come in the middle of a long secular bear or even a secular bull. The chart below shows the three secular bears over the last ninety years.

Screen Shot 2016-01-12 at 8.36.44 PM

As you can see, the defining characteristic for secular bears is that stocks make no progress for long periods of time. Even worse, they experience severe declines which can scar an entire generation of investors. The chart below shows the painful drawdowns investors witness during these secular bear markets.

Screen Shot 2016-01-12 at 8.37.59 PM

The frustrating thing about each and every bear is it’s impossible to know how long they will last. Think about the most recent secular bear, which lasted from March 2000 through March 2013 (I think it’s over, though reasonable people can disagree on this). Stocks briefly poked their heads above their 2000 highs in October 2007 before being slammed right back into their decade long range. Investors had a similar experience in 1980; break above the long range for a minute only to be delivered one final gut punch.

Screen Shot 2016-01-12 at 8.38.28 PM

Looking at bear markets over long periods of time might not be as helpful as breaking them down further. Within the three secular bears have been distinct cyclical bears (think the tech bubble of 2000 and credit bubble of 2007). Here is how I’ve compiled the data below; any time there is a 20% rally, the bear market is over. What this does is break up 1929-1954 period into 11 separate bear markets.

The average of these 20 distinct bear markets saw a 36% peak-to-trough decline, lasting just over 52 weeks. The fifth column shows how long each bear was and the the sixth column shows how quickly stocks gained 20% from their lows, resetting the bear market. For instance, the October ’07 peak to the March ’09 lows was 74 weeks. Stocks then rallied 20% in 4 weeks, making 78 weeks the total length of that particular bear market.

Screen Shot 2016-01-12 at 8.38.13 PM

Whether the S&P 500 sees a correction, and whether or not that turns into a bear market, and how deep it might go and how long it might last is anybody’s guess. The only thing in your control is what you choose to do or not do. Making decisions in the heat of the moment is almost never a good idea, which is why having a plan in place is so important. Knowing that you have an answer, whether stocks go up, down or sideways a is really liberating feeling.

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In the stock market, short term trends are mostly random and heavily influenced by luck

(Source :  http://jimoshaughnessy.tumblr.com/post/137235375474/short-term-luck-versus-long-term-skill )

Daniel Kahneman, one of the fathers of behavior economics, said one of his favorite papers was “On the Psychology of Prediction (1973).” He claims in the paper that intuitive predictions are often unreliable because people base their predictions on how well an event fits a story. In behavioral economics, this phenomena is called a judgmental heuristic—representativeness, or how familiar you are personally with the story. This is one of the worst ways to make a forecast, because it uses a highly limited data set and allows the law of small numbers to mislead you and your forecast. For example, one study showed that when a doctor is told that a procedure works 50 percent of the time (essentially a coin toss probability or base rate) he or she could get the majority of patients to undergo the procedure if he or she simply added “The last patient who did this is doing great!” The story of success eliminates consideration of the base rate.

I recommend that to successfully make predictions about the long-term results of something such as an investment strategy or the overall direction of a market, you must consider three things:

1.      The long-term base rate of the success or failure of the strategy you are evaluating;

2.      The tendency of systems where both luck and skill are involved to revert to the mean and;

3.      What happened historically after certain extreme observations.

So, for example, when I wrote the commentary entitled “A Generational Buying Opportunity” in March of 2009, I was not relying on any particular insight that I might have had at the time, but rather on the data available to me about what happens in markets after they reach an extreme infection point.  It’s important to remember that the stock market is a complex, adaptive system with feedback loops that has elements of both luck and skill. Luck, in the stock market, essentially holds sway over the short-term and is a specific chance occurrence that affects the overall market or individual stock or portfolio can be either good or bad. Luck is a residual—it’s what is left over after you subtract skill from the outcome.

How much luck is involved determines the range of outcomes—where little luck is involved, a good process will almost always lead to a good outcome. Where a measure of luck is involved, a good process will usually have a good outcome, but only over longer periods of time. The luck/skill continuum in investing is almost entirely a function of time. Over shorter periods, your results are highly contingent on luck and chance. This is vital to understand because you might see a bad process provide excellent results due entirely to chance and a good process provide poor results for the same reason.

Consider a simple intuitive strategy of buying the 50 stocks with the best annual sales gains. But consider this not in the abstract but in the context of what had happened in the previous five years:

Year                            Annual Return            S&P 500 return

Year one                      7.90%                          16.48%

Year two                     32.20%                        12.45%

Year three                   -5.95%                         -10.06%

Year four                     107.37%                      23.98%

Year five                     20.37%                        11.06%


Average Annual

Return                         27.34%                        10.16%

$10,000 invested in the strategy grew to $33,482 dwarfing the same investment in the S&P 500, which grew to $16,220. The three-year return (which is the metric that almost all investors look at when deciding if they want to invest or not) was even more compelling, with the strategy returning an average annual return of 32.90% compared to just 7.39% for the S&P 5000. Also consider that these returns would not appear in a vacuum—if it was a fund it would probably have a five start Morningstar rating; it would probably be featured in business news stories quite favorably and the “long-term” proof would say that this intuitive strategy made a great deal of sense and would attract a lot of investors.

Here’s the catch—the returns shown are from “What Works on Wall Street” and are for the period from 1964 through 1968, when, much like the late 1990s, speculative stocks soared. Investors without access to the very long-term results to this investment strategy would not have the perspective that the longer term brings, and without these tools, might have jumped into this strategy right before it went on to crash and burn. As the data from What Works on Wall Street makes plain, over the very long term, this is a horrible strategy that returns less then U.S. T-bills over the long-term. Had this investor had access to long-term returns, he or she would have seen that buying stocks based just on their annual growth of sales was a horrible way to invest—the strategy returned just 3.88 percent per year between 1964 and 2009! $10,000 invested in the 50 stocks from All Stocks with the best annual sales growth grew to just $57,631 at the end of 2009, whereas the same $10,000 invested in U.S. T-Bills compounded at 5.57 percent per year, turning $10,0000 into $120,778. In contrast, if the investor had simply put the money in an index like the S&P 500, the $10,000 would have earned 9.46 percent per year, with the $10,000 growing to $639,144! An investment in All Stocks would have done significantly better, earning 11.22 percent per year and turning the $10,000 into $1.33 million! What the investor would have missed during the phase of exciting performance for this strategy is that, in the end, valuation matters, a lot.

This is a good example of why Kahneman’s paper is so important—people make forecasts not on the data, but how well the prediction fits their perspective and the story behind it. Extrapolating from a small data set can be disastrous to long-term results. The “Most Dangerous Equation” was derived by Abraham de Moivre and states that the variation of the mean is inversely proportional to the size of the sample. A small sample tells you nothing about the true direction of results. Using a small sample—as we see above—can lead to costly errors over the long term.

What this tells us


1.      Investors are well advised to look at short-term performance as a worthless indicator for what will happen over the long-term. Indeed, short-term performance can be among the most misleading to investors and should be heavily discounted. The stock market combines both luck and skill, with luck more pronounced over short time periods, and skill more telling over long periods of time.

2.      Investors should make decisions using the long-term base rates a strategy exhibits—in other words, they should concentrate on what is probable rather than what is possible. If you organized your life around things that might possibly happen to you, you’d probably never leave your house, and when you did, it would only be to buy a lottery ticket. Consider, on a drive to the supermarket, it is highly probable that you will get there, buy your groceries and get back home to unpack them without incident. But what’s possible? Almost anything—it’s possible a plane flying overhead could lose an engine falling directly on your car and instantly killing you. It’s possible another car runs a red light and kills you on impact. It’s possible that you get carjacked and your assailant kills you in the process. You get the point—anything is possible but highly improbable. It’s only when you think in terms of probability that you will get in your car and go, yet few investors do so when making investment decisions. Our brains create cause and effect narratives after something has occurred that seem to make sense, however improbable the event. Witness anyone who invested in the stocks with the highest sales gains after a great short-term run.

3.      In the stock market, short term trends are mostly random and heavily influenced by luck. To succeed, you must ignore them and invest in strategies that have the highest probability (base rate) of succeeding in the future.

4.      You will not win the lottery. Avoid buying tickets and avoid what my son, Patrick O’Shaughnessy, calls lottery stocks.

5.      Over short periods of time, a good investment strategy can lead to poor results just as a poor investment strategy can lead to good results. Do your homework; understand how a strategy performs over long periods of time and stick with it. If you can do just this one thing, you will be ahead of the vast majority of investors over the long-term.

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No surprise, a lot of unicorns are actually donkeys.


( Source : https://medium.com/@abhasvc/unicorns-vs-donkeys-your-handy-guide-to-distinguishing-who-s-who-f1b30942b2b6 )

I’ve been having this conversation a lot lately:

Friend: “Did you see [startup] just raised at a $1B valuation?”
Me: “Unbelievable.”
Friend: “They’re apparently killing it on [metric that is meaningless without the bigger picture].”
Me: “Yeah, but their [metric that also matters] is struggling.”

I am by no means the unicorn prophet, but here’s how I think about which companies have earned their unicorn status vs. which ones are playing a dangerous game of massive capital needs, sky high valuations, impossible expectations, and deferred judgement days. Hopefully, by the end of this post, you’ll have an intuitive feel for which startups actually have a shot at being unicorns and which ones are probably just donkeys.

The Fundamental Law of Growth

LTV = Lifetime Value of a Customer; CAC = Cost of Acquiring a Customer

Like Newton’s laws of gravity or momentum, most tech startups (see exceptions below*) who sell directly to their customers — both enterprises and consumers — must eventually obey the Fundamental Law of Growth: LTV/CAC > 3. There’s a lot of nuance as to why — a discussion that is better suited for a semester-long class than a blog post — but suffice to say that the LTV/CAC ratio speaks to a startup’s revenue trajectory, capital needs, and in turn, how much “irrational exuberance” is demanded of its investors. The lower the LTV/CAC ratio, the less efficient a company is at deploying capital and the more money it needs to fuel growth; conversely, the higher the LTV/CAC ratio, the more efficient the company is and thus the more value it creates for the same amount of capital. Though this can be derived, many before me have empirically observed that 3x is roughly the threshold needed to build big, sustainable businesses.

Assessing a company’s valuation is a discipline on its own and growth is only one factor in that calculation. However, for simplicity’s sake, one can assume that tech companies who don’t obey the Fundamental Law of Growth will eventually lose access to capital, drastically slow their growth, and watch their valuations plummet — those fabled unicorns will eventually emerge as donkeys. So with that, let’s dig into some examples…

*Companies whose value is not predicated on revenue (e.g., disruptive technologies, monopolies, social networks, intellectual property) as well as companies where revenue is achieved indirectly (e.g., ad-tech networks, certain marketplaces, certain viral growth startups) or discontinuously (e.g., government contractors) typically do not follow this rule

For each example, I’ll make assumptions about the various components of the LTV/CAC ratio (see below); some assumptions are based on publicly available data and others are just gut feels. If it’s the latter, I’ve generally erred on being generous to the startups.

ARPU = Average Revenue Per User

Case Example #1: HelloFresh, Subscriptions Meals

  • Customer Lifetime — in my household, we usually try each meal subscription company for a few weeks then switch it up, but let’s assume the average across all customers is 3 months or 0.25 years
  • ARPU — average revenue is probably 2 people, 3 meals per week, 3 weeks per month, so $60/week x 3 = $180/month or $2160/year
  • Margin % — we know from Mahesh’s excellent IPO filing teardown that their margin is 52% (sign of a strong operating team; that’s higher than I expected for this type of business!)
  • CAC — given the numerous other meal subscription companies, brick and mortar competitors, etc., it feels like the CAC is probably in the hundreds, say $400
LTV/CAC = 0.25 years x $2160/year x 52% / $400 = 0.70x

Under these assumptions, HelloFresh is an incredibly capital intensive company because of the (presumed) low customer lifetime/high churn. We know from the IPO filing that HelloFresh grew its revenue from $77M in 2014 to $290M in 2015 (276% growth), so you can understand why someone would say, “They’re killing it on revenue!”. We also know that the company didn’t report cohort retention data, but as per Mahesh, “they do mention that they achieve 2.8x LTV/CAC after two years.” Hold up, come again?Reporting LTV/CAC for only a subset of customers is disconcerting, and even then, it’s just under 3x; substituting 2 years into the LTV/CAC ratio suggests that the true CAC may be much higher ($800). Other food subscription and even some on-demand meal companies — Blue Apron, Plated, Instacart, Munchery, Sprig, etc. — may similarly have short customer lifetimes/high churn and thus low LTV/CAC ratios, thereby also violating the Fundamental Law of Growth.

Verdict: Donkey Watch

Case Example #2: Evernote, Productivity Software

  • Customer Lifetime — I use Evernote constantly, so I expect if anyone is going to have an extended lifetime, it’s them. But as a rule of thumb, lifetimes >3 years should only be considered in exceptional circumstances
  • ARPU — in most freemium products, paid customers make up only a tiny fraction (<5%). Nevertheless, let’s assume 25% are premium users at $50/year, so a blended ARPU of .25 x $50 = $12.50
  • Margin % — pure SaaS company with no customer service costs should probably achieve 70–90% margins, so let’s go with 90%
  • CAC — freemium models typically land in the $1–$100 CAC range, so let’s assume $20
LTV/CAC = 3 years x $12.50/year x 90% / $20 = 1.69x

Evernote has great customer lifetimes, margins, and low CACs; however, because their pricing is low, their overall LTV is limited and thus results in a low LTV/CAC ratio, again violating the Fundamental Law of Growth. Evernote could compensate by increasing pricing, but with other readily available substitutes (Google Docs, Microsoft OneNote), increased pricing likely increases churn too, so the pressure is on Evernote to then increase ARPU by increasing value (additional products, collaboration tools, AI insights, etc.).

Verdict: Donkey Watch

Case Example #3: Oscar, Health Insurance

  • Customer Lifetime — once you join an insurer, you typically stay with them until you switch jobs/get a job. <1.5 years is probably the average, but let’s use 2 conservatively
  • ARPU — $5000; saw this in an Oscar press release and it’s fairly typical of this market
  • Margin % — healthcare insurers have gross margins in the 5–10% range with a max of 15% as mandated by Obamacare, so let’s go with 15%
  • CAC — this is an expensive product for consumers to purchase and probably requires a light-touch inside sales team, so let’s assume CAC is $800
LTV/CAC = 2 years x $5000/year x 15% / $800 = 1.88x

Similar to HelloFresh, Oscar is posting massive revenue ($200M) and growth rates (135%), so you can again understand the hype around them; however, Oscar fails the Fundamental Law of Growth due to its low gross margins. If the Oscar team can achieve a CAC near $500 — perhaps because they’re the hip/fresh insurer on the block with best-in-class marketing — then maybe the company can still grow a horn, but that’s asking a lot given the inherent complexity and cost of the product. Recently, a number of other companies— Jet.com, Instacart, etc.— have built fast-growing businesses that operate on low margins, but they too are at risk of breaching the Fundamental Law of Growth.

Verdict: Donkey Watch

Case Example #4: ZocDoc, Online Physician Reservations

  • Customer Lifetime—I’ve heard that physicians typically churn after a year once they’ve established a sizable patient base, but let’s assume 2 years
  • ARPU — $3000 (publicly available)
  • Margin % — SaaS company with light-touch customer service should probably achieve 60–80% margins, so let’s assume 80%
  • CAC — Selling to physician practices must be challenging, so like any high-touch inside sales operation, ZocDoc’s CAC is probably in the $1–10K range; let’s assume $3K
LTV/CAC = 2 years x $3000/year x 80% / $3000 = 1.60x

ZocDoc has a good LTV overall, but their CAC is likely a show-stopper. Unfortunately, there’s no getting around that — selling to physicians is tough stuff, just ask Pfizer. Also, as competition increases, customer lifetimes and pricing erode too, further driving down the LTV/CAC ratio. I suspect this is why ZocDoc is shifting sales to hospital system customers (1000x higher LTV and only 20x higher CAC), but hard to know what fraction of their business this constitutes. Although I am not familiar enough with the unit economics of fantasy sports startups, I suspect that FanDuel and DraftKings may similarly be spending heavily on customer acquisition without the supporting customer lifetimes or ARPU needed to satisfy the Fundamental Law of Growth.

Verdict: Donkey Watch

Concluding Thoughts

I hope this framework gives you a better sense of how to evaluate today’s unicorn landscape. The companies above all have impressive, press grabbing growth metrics, but they also fail the Fundamental Law of Growth for different reasons — short customer lifetime, low pricing, low margin, and high CAC — so must be viewed with some skepticism.

The most obvious next question is: if the Fundamental Law of Growth is so simple, why did investors grant $B valuations to these companies and others in the first place? I believe the answer is a combination of downside protections, upside overoptimism, and what can only be described as FOMO.

Downside protections are being prominently discussed now in light of Square’s down round IPO (albeit still in unicorn territory); to put it simply, late stage investors have (smartly) insulated themselves from losses, so they’re willing to give more on valuations. With regards to upside overoptimism, I imagine that when these rounds were executed, both investors and entrepreneurs believed that things would look up — customer lifetimes would extend, ARPU would increase, margins would expand, and CACs would decline. Alas, it doesn’t always pan out that way, which is why we encourage our portfolio companies to stay conservative on valuations: big up rounds can be appealing in the short-term, but when companies stumble (which they often do), the subsequent down rounds can be outright devastating. Zenefits, for example, is likely to feel that pain shortly given their recently exposed stumbles.

Personally, I’m looking forward to a private market correction. I feel my colleagues and I have done a good job building a portfolio of companies with sound fundamentals and well-earned valuations; a return to sanity would be a welcomed change, as it would unlock quality talent that we can then direct to our companies and others who are playing the prudent, long game.