Two Different Types of Relationships Between Prices and Information

In financial markets, one observes two different types of relationships between prices and information.

  1. Type D for Deductive: Consider the available information and then calculate a price. The classical model of how to value things.
  2. Type I for Inductive: Consider the price, assume it contains informational content, and derive the information from the price.

In theory, financial markets operate mainly with Type D, but I think in practice, markets operate mainly with Type I. In the real world, one observes investors and analysts assiduously building models to explain and thereby justify prevailing prices. Paradox? The more one believes the market is Type D, aka the EMH, the more the market actually behaves as Type I. The more you believe the market is efficient, the more information you assume is embedded in market prices.

Hence, the more information you assume is embedded in market prices, the more you operate as Type I, inductively reasoning from prices. Therefor, the more one believes the rest of the market is Type D, the more likely oneself is Type I. Hence the widespread belief in market efficiency leads to inefficiency, as investors reason from prices vs from a priori information?

Is this a robust explanation of boom and bust cycles within a market in which most investors are trying to be rigorously logical?

Source Tweets: 1,2,3,4,5,6,7,8,9,10,11

Hedge Fund Baron Paul Singer

Hedge fund baron Paul Singer: “London, Manhattan, Aspen, and East Hampton real estate, and art, prices [show] leading edge of hyperinflation.”

There are a bunch of reasons to believe that his theory is wrong, but one that I think is under-appreciated is this:

Those are specific markets seeing a dramatic influx of ultra-high-net-worth buyers from overseas–China, Russia, and certain other nations. You can see this vividly in this week’s art auctions in New York, and it’s been obvious in London real estate for some time.

Another: “30% of all apartments 49th-70th Streets between Fifth and Park are vacant at least 10 months a year.”

So price rises in these specific assets can likely be explained by simple supply and demand without requiring inflation, hyper or otherwise. Therefore, price rises in these assets do not necessarily indicate much of anything about the domestic macroeconomic situation.

In fact, an obvious “bullish on America” argument here: Capital fleeing other countries and landing specifically in US (& UK!) real estate. Further, these asset prices may be explainable quite independently of the various QE and inequality debates happening within the US.

On a global economic scale, the total value of this specific real estate in addition to art is not that large. Prices are easily altered by capital flows.

Source Tweets: 1,2,3,4,5,6,7,8,9,10

The Challenge of Threading The Needle

In response to Cash Burn Rates at Startups, one of the responses was a question asking, why isn’t this just hypocritical venture capitalists overfunding reckless founders of out-of-control startups? In fairness, there is probably some of that, though we and the investors we respect try hard not to indulge in recklessness and irresponsibility. But while it’s irresponsible to vaporize cash and your company, it can also be irresponsible to NOT invest to become #1 in a big new market. Particularly now, since there are SO many more people on the Internet and SO many more businesses that can consume cloud/SaaS vs 15 years ago.

Tension: Over invest, escalate burn, risk down round, vaporize when the market turns OR under invest, starve growth, don’t win the market and implode. Why is this so important? In tech-driven markets, the overwhelming economic returns tend to go to the company with the highest market share and the winning company with the highest market share can invest the most in research and development to build the best and most advanced products. This is the prize.

Via Glengarry Glen Ross: The reward for market position #1 is 90% of the economic value. #2, a set of steak knives. #3, you’re fired.

The challenge for CEOs and boards of tech startups is to thread the needle. Make just enough of an investment to take the #1 position, but not more. Meeting this challenge has resulted in thousands of venture-capital-backed companies creating millions of jobs over the last 50 years. This challenge becomes more difficult when money is flowing freely, since more competitors get funded. It’s very tricky and requires deep judgment. BUT opting out of the race generally guarantees you won’t be #1 or even #2. It’s not a good idea either and is just as serious a risk as blowing up.

There is no single answer. It’s up to VCs, CEOs, boards, and later-round investors to think very carefully about this for each specific circumstance.

Source Tweets: 1,2,3,4,5,6,7,8,9,10,11,12,13

Cash Burn Rates at Startups

Recently and have sounded a vivid alarm. I said at the time that I agree with much of what Bill says and I want to expand on the topic further. New founders in the last 10 years have ONLY been in an environment where money is always easy to raise at higher valuations. THAT WILL NOT LAST. When the market turns, and it will turn, we will find out who has been swimming without trunks on. Many high burn rate companies will VAPORIZE.

High cash burn rates are dangerous in several ways beyond the obvious increased risk of running out of cash. It’s Important to understand why:

  1. High burn rate kills your ability to adapt as you learn and as the market changes. The company becomes unwieldy and too big to easily change course.
  2. Hiring people is easy; layoffs are devastating. Hiring for startups is effectively a one way street. You can’t change once you’re stuck.
  3. Your managers get trained and incented ONLY to hire, as the answer to every question. The company bloats and becomes badly run at same time.
  4. Lots of people, a big shiny office, and high expense base equals a fake we’ve made it! feeling. This removes the pressure to deliver real results.
  5. More people multiplies communication overhead exponentially which slows everything down. The company bogs down and becomes a bad place to work.
  6. Raising new money becomes harder and harder. You have a bigger bulldog to feed, need more and more cash at higher and higher valuations. Therefore you take on an escalating risk of a catastrophic down round. High-cash-burn startups almost never survive down rounds. They VAPORIZE. Further, to get into this position, you probably had to raise too much cash at too high a valuation before; this escalates the down round risk even further.
  7. Even if you CAN raise an up round, you are increasingly likely to incur terrible structural terms like ratchets to chin the bar. That nice hedge fund investor willing to hit your valuation bar? Imagine him owning 80% of company after a down round. How nice will he be then?
  8. When the market turns, M&A mostly stops. Nobody will want to buy your cash-incinerating startup. There will be no Plan B. VAPORIZE.

Finally, there are exceptions but if you’re reading this, you’re almost certainly not one. They are few and far between. Worry.

Reference Material:

Source Tweets: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18

Andreessen Horowitz Invests $50 Million In BuzzFeed

Tonight I’m tickled pink to be able to talk about our new investment in BuzzFeed! My partner discusses our new investment in BuzzFeed here.

We think of BuzzFeed as a technology company. They embrace Internet culture. Everything is first optimized for mobile and social. BuzzFeed has technology at its core. Its 100+ person tech team has created world-class systems. Engineers are first class citizens.

On top of its technology core, BuzzFeed’s reporting team is now routinely committing breathtaking investigative journalism.Here are a few examples.

We think the opportunity in front of , , , and their colleagues is effectively unbounded. We are very excited to work with everyone at BuzzFeed to help them realize their dreams of a profoundly important new media institution.

Source Tweets: 1,2,3,4,5,6,7,8,9,10,11,12


Harvard Business School Publishes New Study On The Interplay Between Diversity and Success In VC Investing

The Harvard Business School has published a new study on the interplay between diversity and success in venture capital investing in startups.

The more ethnic and educational affinity there is between two venture capitalists investing in a firm, the less likely the firm will succeed. Venture capitalists have a strong tendency to team with other venture capitalists whose ethnic and educational backgrounds are similar.


Two VCs from the same undergraduate school are 34.4 percent more likely to collaborate and increase by 39.2 percent if of the same ethnicity.


The odds of success of an invested company go down 17% if two VCs worked at the same company; -19% if the same undergrad school; -20% if the same ethnicity. The lack of success among similar investors seemed to lie in the decisions that *followed* the investment due to ‘groupthink’.

Side note: Last names of the three researchers who wrote the paper — Gompers, Xuan and Mukharlyamov.

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Source Tweets: 1,2,3,4,5,6,7

Cybersecurity Firm Tanium Receives $90M Investment

Today we’re thrilled to be able to talk about our newest investment,Tanium. We’re investing $90M. The first venture capital investment since it was co-founded in 2007. Collectively, partners at have maybe 200 years of experience in systems management; Tanium is a breakthrough like we’ve never seen. Tanium –> people responsible for large networks of computers and software, what Google –> people on the Internet. I don’t say that lightly.

The company is operating under the radar, but its customers could not be more enthusiastic. The product must be seen to be believed. It’s amazing. Tanium is a real-time query *and* control–100s of Ks of computers and virtual machines with natural language queries.

We are thrilled to be working with genius founders David and Orion Hindawi and their colleagues. Masters of the art and science of computing. More info: or email

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Source Tweets: 1,2,3,4,5,6,7

The Risk and Return of High-Tech Startups and Venture Capital

Heading into our 5th anniversary Annual Meeting next week, the risk and return of high-tech startups and venture capital on my mind…

My rough estimate of my personal “success” (positive vs negative outcome) rate of bets on new products and companies is ~60-65%. Meaning, of course, my “failure” (negative vs positive outcome) rate is ~35-40%, i.e. I’m wrong about several things at all times.

Per Tversky/Kahneman loss aversion, the negative outcomes weigh much more heavily on my mind. The key challenge = manage own psychology. Per Taleb antifragility, the saving grace of my business is: Each loss capped at 1x, but wins can scale to 1,000x and even beyond.

I am also blessed in having partners and colleagues over 20 years and today who are often better at picking than I am. I envy my hedge fund friends who can fully implement “strong views weakly held” and trade out of bad positions any time they want. However, because we make hard commitments for 10+ years, we can win from sleeper hits that take longer to develop. That’s very satisfying.

On balance, progress is made. But the emotional rollercoaster never stops! Our entire field is constantly exhilarating and terrifying. The other saving grace is seeing people we work with develop, succeed, and flourish. Enormously fulfilling, makes it all worthwhile.

Commenters correctly point out that for VC (more than other fields), to calculate “success” rate, must account for great opportunities passed, i.e. total “at bats” = decisions made to pull trigger whether they worked or not, plus decisions to pass on big wins one could have had. So “at bats” probably more like 120%-140% of # of decisions made to invest. It knocks the overall “success” rate down a fair amount. Humbling!

The counterargument against counting “misses” is that success in investing has nothing to do with hitting every good opportunity. But still! 🙂 And ultimately, in both startups and VC, “success” rate (batting average) means nothing; slugging percentage means everything.

Source: Tweets – 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15