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.