One of the things which confronted me when I got interested in quantitative finance is the varieties of different kinds of quant. Now I realize this is pretty simple. Quants come in three basic varieties.

Structurers: people who price complex financial instruments. Risk managers people who manage portfolio risk Quant traders people who use statistics to make money by buying and selling

It took me quite a while to figure this out. I don’t know why people haven’t bothered to state this taxonomy of quant jobs. I suspect it’s because most quants are structurers. Of course, there is often bleed over between these varieties -but it’s a useful taxonomy for looking for work. I’ve done a little of all three at this point (very little, honestly), and have always liked quant trading problems more than the other two varieties. It’s the most ambitious, and the most likely to net you a career outside of a large organization (go me: Army of one!). It’s also the most mysterious, since successful quant traders don’t like to talk about what they do. Structurers and risk managers have to talk about what they do, almost by definition. Quant traders gain little from talking about their special sauce. The ones who have spilled the beans are guys like Ed Thorp -who only talk about old strategies, or guys like Larry Harris, who wrote the best book there is on trading, though he wrote it without any interesting equations in it. Of course, there are going to be quant jobs which don’t fit exactly into these categories; there is a lot of overlap between traders and risk managers, for example: I’m only presenting them as a useful framework to hang some thoughts on.

Since I’m not presently employed as a quant trader, I don’t mind talking about it a little bit. I hope to outline below a rough but mostly complete taxonomy of how this stuff works. Later on, I might outline some specifics of how these basic ideas are applied in practice.

To make money as a trader, assuming your motivation is to make a profit, you need to buy low and sell high. That’s really all there is to it. Losing sight of this is the source of much trading ruin. People often lose sight of this. They build spectacular technical analysis models based on … whatever wavelet/fractical special sauce they can dream up, and forget that they are supposed to be buying low and selling high. To do this, you need some kind of insight or information that other people in the market lack, or you need a structural edge which other market participants don’t have. The latter brings me to the first kind of trading strategy in my bestiary:

Liquidity peddlers. Market makers earn the spread. What does this mean? They will display prices for buying and selling an instrument, the difference of which is the “spread.” In an ideal situation, this means they’ll buy from people who want to sell, then sell the same thing to someone else who wants to buy, earning the price difference. They’re taking a risk on that there will be no seller or buyer on the other leg of the trade, and that the price will move against their resulting position. If you’re not competing with other people who do this, you can make tidy, low volatility profits. If you have a captive audience, you’re not competing with others who do this, and so this is a pretty good trade. This is what most people think of as “liquidity provision” or “making markets.” How this gets done may be as simple as what I just outlined, or it can get very complex indeed.

This is why liquidity is good

Arbitrageurs earn a different kind of spread. These guys rely on a structural advantage of some kind. It may take the form of having very fast software. It may be because they are large market participants in geographically distant market locations. It may be because they happen to own a large boat with oil in it, and they drive the boat to the place they’re most likely to get a nicer spot price. Sometimes they arb things which are identical: FOREX futures for example. Sometimes they arb things which are supposed to be identical, like index futures versus index ETF’s. And again, sometimes it gets complicated. Statistical arbitrageurs are a sort of squishy area, similar to arbs, but distinct from them. They find “pieces” of securities which are theoretically equivalent. For example, they may notice a drift between prices of oil companies which should revert to a mean value. This mean reversion should happen if the drift doesn’t have anything to do with actual corporate differences, like one company’s wells catching on fire. What you’re doing here is buying and selling the idea of an oil company, or in other words, a sort of oil company market spread risk. You’re assuming these two companies are statistically the same, and so they’ll revert to some kind of mean when one of the prices move. Similarly, in the merger between two companies, there is a risk spread in the relative values of the stock prices of the companies, lest the merger doesn’t go through. If your statistics or inside information is good enough, you can buy this spread at a profit. In some sense, statistical arbitrageurs are a hybrid of liquidity peddlers and arbitrageurs. They earn their money in both ways. Fundamentals traders: these guys are trying to be the electrical version of Warren Buffet. They buy what they consider “bargain” stocks, and hold until they can realize some kind of profit, either from harvesting dividends, or from the price appreciation of the stock. This gets a lot of press as being very subjective, but it can be entirely quantitative. Indeed, I suspect Buffet, like most traders, at least uses a quant screen to make his picks. Many, many hedge funds are buying and selling stocks based on accounting data, market trends, and other such information which may or may not have been baked into the price at any given moment. Liquidity providers and statistical arbitrageurs prey on fundamentals traders (among other people). Since fundamentals traders want to control large blocks to harvest large returns, they have to pay for their liquidity. This is the style most people think of as “buying and selling stocks” -it looks a lot like investing. The ironic thing about it is, this is also the area where most money is lost in algorithmic trading. When you hear about quant funds crashing all at once like in 2007, this is what they’re talking about. It will be a bunch of funds going after the same opportunities in ops cash flow versus accruals, price/book ratio or sector momentum stocks … then something in the market goes not according to the model. Since these guys all use the same dumb quarterly rebalanced models, and the market value of the firms invested in is very large, lots of money gets lost. Arbs, liquidity peddlers? The amounts of money involved are much smaller. Arbs and liquidity peddlers can be one or two or five man operations with only a little money in the bank. That’s why guys like me get upset when media fiends go after “high frequency.” You know who they’re going after? The small businessman; aka me, that’s who. Little guys like me who didn’t go to Yalevard and don’t have any bazillionaires on the rolodex can still make a living as arbs or liquidity peddlers. Oh, the media makes it sound like they’re going after bloated behemoths like Goldman. I wouldn’t be surprised if such companies were actually behind this fake “populist uproar.” Goldman are certainly a lot more likely to profit from changing legislation or exchange rules than I am; that is why they seem to be all for new regulation. I guess fundamentals traders can also be small firms, though they’re going to be much more easily wiped out than a large firm due to the volatility inherent in such strategies. But it is worth realizing that virtually all large algorithmic firms are fundamentals traders. This is true because fundamentals trading is the only kind with the large capacity required to cut lots of paychecks on a 2/20 deal with investors. I have to admit some bias here: some fundamentals algo trading annoys the hell out of me. A lot of what they do isn’t much more sophisticated than picking stocks by price/earnings ratios. IMO, there should be Yahoo finance style services for individual investors to pick stocks in the same way these guys do. I have considered building one and making money selling ads the way Yahoo does. Selling ads on a finance webpage is a much lower volatility business than seeking alpha in yet another low Sharpe fundamentals trading business. Probably ads more value as well. This is why it’s good to be a fundamentals trader: lots of company Less pertinent to profit making algorithmic trading strategies, but worth mentioning anyway: Hedgers: are not necessarily interested in making a profit by executing a trade. They’re more interested in trading as a form of insurance. The insurance generally locks in a profit, or minimizes losses someplace else in a business, but the hedging trade itself is not meant to be profitable. Much of the options, short interest and index futures market consists of people who are buying a form of market insurance. I partly mention this category out of historical interest: people sometimes blame the crash of 1987 on a hedging algorithm in common use in those days. Still, profitable trading strategies often contain hedges, and hedging is a large part of trading volume, so it is worth mentioning explicitly. Hedgers can also be a great source of profits. While this may sound very “tooth and claw” -you can also look at it as providing hedging services to people who want to be hedged. Noise traders: many trading algorithms don’t make any sense from the profit making or hedging point of view. The most obvious category here are idiots with computers who don’t know what they’re doing. Less obvious than this are index ETFs; all they want to do is track an index. This is harder than it sounds; in fact, it is NP hard. Another not-so-obvious category are central banks trying to manipulate their currency prices. While this is a rather grab-bag category, I have to put these guys somewhere. While I don’t know any central bankers, looking at Forex ticks, what they’re doing appears to be at least partially algorithmic. Noise traders are also a great source of profits. They are also a very large fraction of trading volume.

My categories are somewhat arbitrary, just as my aforementioned categories of quant are somewhat arbitrary. Again, these categories are to hang thoughts on. I’m pretty sure you can chop any quantitative trading system into one or more of these categories as a useful way of thinking about things. For example: let’s say I’m trading on the spread between an actual basket of stocks comprising an index, and an ETF or index future. I’d categorize that as statistical arbitrage. Sure, people have a special name for it: index arbitrage, but it’s really a kind of stab art, the same way as trading pairs is: you’re just trading a lot more pairs. But wait: it could get more complicated. Let’s say you want to build an index better than the actual and use the index to hedge risk: is that still stab art? Or is it more like fundamentals trading? Really, it is a bit of both. To build an index beating portfolio (usually a subindex), you need to do some fundamentals modeling. Is the Tu Jeng Hound of the Hedges plant or an animal? Maybe he is a little of both.

Which of these are high frequency? All of them. That is not an exaggeration, though I am mostly saying it to make clear that I’m not breaking things down by time scale. People like Warren Buffet may have a long investment horizon, but they also need to pay as little as possible for liquidity. Shopping the block is a non-trivial problem. Speed is important in all kinds of trades. Latency isn’t always that important, however. For example, while speed is probably important in your hypothetical index arb problem, I’m pretty sure latency is a secondary consideration. Latency of course is important in pure arbitrage and most kinds of liquidity provision.

What is a “predatory algorithm?” Generally speaking, this is an algorithm that makes your life inconvenient on any of these trades. Seriously: that’s what the phrase means. Anyone who tells you otherwise is selling something.