Archives For October 2010


October 20, 2010 — 8 Comments

I think that, sometime soon, people are going to stop making predictions.

Actually, tragically, that isn’t likely to happen. Human beings are seemingly irrepressible predicting machines, in that it is a way to sound intelligent without anyone being able to immediately call bullshit. By the time the future arrives, most people don’t care enough to back-test you. I was jolted into reconsidering this issue by the recent news from England, that they are going to go into austerity mode in order to reduce their national debt. Personally, this feels smart to me, in a “I’m no expert but isn’t balancing your books a good idea?” kind of way, but you can definitely find any number of economists violently committed to either side of the debate.

This is an old story, but it bear repeating: At the start of the year, a stockbroker sends a letter to 1,000 prospective clients, each containing his recommended “best pick” for the coming month. He sends a different ticker symbol to each of 10 groups of 100 prospects. In February, after 5 of the 10 stocks perform above average, he targets those 500 remaining prospective clients, further dividing them into 5 groups and repeating the trick. By June, assuming the same level of performance (ie, 5 of 10 picks perform above average), he has 30-odd prospective clients who think he’s a genius. Rinse and repeat, and you’re in business.

How different is that from Roubini and the rest of the economists out there? The bulls were right for the better part of a decade, until they were horrifically wrong, and the bears have famously predicted 7 of the last 2 recessions. Maybe it was always thus, or maybe it’s newly true, but I think at this point we can conclusively say this: the world is too complicated to predict. There are too many variables to fit in any one model. Just focus on what you know, do a good job, try to add value, say/do something novel, don’t listen to the experts, and you’ll be fine.

When I was an undergrad, in the early 1990s, I spent a little time trying to figure out how to invest what meager savings I had (generated by my lucrative summer pizza delivery gig). My first efforts were directed at figuring out what public equities I might invest in. [Sidenote: Ultimately I ended up putting all of my savings in my roommate's start-up, which I do not recommend but which did end up allowing me to buy a house five years later.] I ordered a bunch of annual reports from the investor relations departments of companies I was interested in, got a pencil and some graph paper, and did some rudimentary calculations, like: how fast are these companies growing? what are their PEs? Obviously this seems ridiculous to those weaned on Yahoo Finance, but trust me, much of what I was doing those days would seem ridiculous to most sensible people.

My point is this: even as a 19 year old liberal arts trained neophyte, my first instinct upon deciding to invest in stock was to do some math. So when I read all of these people attacking “quant traders”, I have to wonder: as opposed to what? Instinctive traders? Psychic traders? Insider traders? As someone who now spends his time figuring out how to invest in financial services start-ups, understanding the actual contours of this new phenomena is important to me, so I’ve done some thinking about it. I will leave the question of who to demonize up to you, but I will do my best to lay out some frameworks that hopefully introduce some nuance into the debate.

In actual fact, most of what people are complaining about when they decry quant trading is actually a sub-set of quantitative trading (admittedly the largest part) called high frequency trading or, somewhat interchangeably, low latency trading. In fact, these are two different techniques often used in combination: High frequency = lots of trades/second, Low latency = getting the trades into the marketplaces very quickly. High frequency/low latency trading marries two skill sets: a)an analytical pursuit of transient pricing anomalies and b)a hardware/software/communications configuration designed to get trades into the various markets more quickly than the next guy. Part (b) is important because of the key word “transient” in part (a). This kind of quantitative trading has been around for decades, since the advent of computers essentially. Firms of this type input massive amounts of market data, across all types of securities and geographies, and then look for correlations, eg, if the price of this commodity future goes up 5%, the stock price of this Indian company should trade down 2%. [note: this example is obviously over-simplified to the point of parody, as will be other examples, so please check your condescending vitriol at the door, if you can.] They carefully back-test these observations to determine their validity and robustness over time, then build trading strategies built around looking for events in the future that mirror these historical correlations.

The problem with this type of quant trading is that, over time, with everyone working on the same data set, everyone makes these same observations. So then the question becomes who can trade on the data first. Hence the massive investment in infrastructure to turn these quantitatively-derived investment ideas into low latency trades. What is important about that is one quickly realizes that in order to minimize cycle time, you need to cut the slowest link in the chain out first: the human brain. As a result, we now have computers trading directly with other computers, and this has created many of the market structure issues we are dealing with now, such as “flash crashes” and high levels of volatility. In addition, even if you get there first, the profits available in a given trade are often tiny. Hence the other customary component of this trading style, the high frequency part: if you’re making a penny per trade, you’ve got to do a lot of trading.

But let’s back up a second. The reason that this branch of quantitative trading led to high frequency trading is that, in a sense, the observations are “obvious” (at least if you have $100MM worth of computing power and all of the market data in the world.) As such, making the observations is a commodity, albeit an expensive one; it is trading first on them that is the money-maker. But what about types of quant trading that are predicated on making investment decisions that are non-obvious? Specifically, investment decisions based on information that is coming from outside of the markets, versus strictly from inside the markets, ie, prices.

Quite clearly, that is what most great equity investors do. They get to know a company well, analytically and otherwise, make a prognostication about the future of the company and then buy or sell the stock when the rest of the world disagrees with their prognostication. The frequency of their trades can be high or low, depending on how quickly it takes for the rest of the world (ie, the markets) to figure out that they were correct. Another simplistic example: I think Coke is worth $70, and it’s trading at $67. I buy at $67 with a plan to sell at $70, regardless of whether it hits $70 in 10 minutes or 6 months. If i’m truly disciplined about that, this could be a high frequency trade indeed, if the market comes to agree with me quickly.

Our view is that the next great revolution will be applying the information technology techniques of high frequency trading to this kind of non-obvious investing, which relies on the intake and synthesis of exogenous data (from outside the markets) to make pricing observations. In that these kinds of observations will have a kind of duration durability far in excess of endogenous observations (based on readily available market data), they will generally be far less dependent on speed, and as a result not destabilizing. These new types of firms will use computers to enable and validate human investing intuition, rather than using computers to try to be first past the post in a race to the bottom. Two of the emerging winners in this space include Two Sigma and Kinetic Trading. I would humbly submit that these new firms should be called quantitative investors, rather than quantitative traders, and I look forward to backing some of the best of these players.

I was talking with one of my favorite entrepreneurs the other day, who was trying to choose a lead for his next financing round.  This is a guy who had plenty of options.  Facetiously, sort of, he said he was planning on picking the VC who had the most unique visitors to his blog.  That, of course, sent a chill down my under-publicized spine.  Again, i think he was kidding, but also kind of not.  He went on to explain that his biggest single job, and therefore problem, is recruiting, and a VC who can help him tout his company, and add credibility simply through association, is a major asset.

My response:  kill me now.

The fact is, relatively few venture capitalists I know went into this business because they were self-promotional.  Most of us are geeks who like technology and like hanging out with people who create new things.  The overlap between that personality type and the kind of folks who go on reality TV shows is roughly zero.  Despite that, a wave has hit our industry; a wave that previously hit the media industry and will go onto spill more broadly into corporate America.  The old methods of being successful, which were predicated on hand-crafted, person-to-person networking and leveraging the brand name of your firm, have been supplanted by the need to build your own profile online.

This has been playing out in the media for a few years.  As the traditional media brands totter and cast about for a business model, the onus has fallen more on more on the “talent” to forge relationships directly with their audience.  It is less the case that George Stephanopoulos works for ABC than that he leverages his position at ABC to build a profile for himself (and garners nearly 2MM followers on Twitter in the process.) The days when a cub reporter could get a job at the New York Times and simply do good work are, sadly, behind us.  That cub reporter had better be on Twitter, Facebook, Tumblr, etc, building an audience and creating quasi-personal relationship with her followers.  When her contract is up, she had better be able to point to the thousands of people who will follow her to her next gig.

This is becoming more and more true in the venture business, starting with those VCs that invest in social media, and moving beyond that sector.  This trend has challenged some of the incumbent leaders, many of whom are still on the sidelines as it relates to social media, and created room for new players.  Mark Suster is relatively new to the venture business, but has build a profile for himself that is second to few.  Having said that, I’ll bet if you quizzed 100 of his Twitter followers, fewer than half could name the firm he works for (GRP, by the way, who have been killing it lately.) This is a disruptive moment for our industry, and the new leaders will be individual VCs, rather than firms.

What’s next?  I think corporate America.  As with media and venture capital, right now having a well-known social profile is just an opportunity, not a threat.  Tony Hsieh at Zappos created equity value for his shareholders by being early and compelling on Twitter, but other CEOs are not yet feeling the pressure to follow.  That will change.  In 10 years, I believe that consumers will bias towards buying products (and retail investors will bias towards buying stock) from companies whose CEO they “know”, and have an online relationship with.  Personal publishing will move from being an opportunity, to a competitive advantage, to an absolute necessity.

As for me, I feel late but I’m running quickly.  Twitter is a better fit for me than blogging, as my musings tend toward the short and insubstantial.  [I feel certain my partner Bo would interject here that "short and insubstantial" could actually BE my personal brand].  I had a meeting today with an angel investor who I’d known previously only through Twitter, and I felt like we skipped forward at least two meetings worth, based on seeing each other’s faces every day.  I love the opportunity to praise and comment on my companies.  I continue to believe that there is no substitute for the “old” ways of doing things:  doing great deals, being a good guy and getting out and meeting people.  But there are new ways, too, and we ignore them at our peril.