Algorithmic Trading

'Algorithmic trading' is trading done by computer programs (algorithms) that pick up on trade-worthy bits of information from streams of market data and financial news. According to a recent article in The Economist, algorithmic trading accounts for a third of share traded in the U.S. and analyst project that algorithmic trading in some form or fashion will grow to account for a majority of trading in the coming years.

One of the advantages to algorithmic trading is sheer speed. In trading, especially in 'statistical arbitrage,' even a few milliseconds can mean the difference between exploiting a profitable opportunity or missing it altogether.

There are problems with this type of trading. One of the problems is that the algorithms may mis-interpret data or not have enough or the right kinds of information to make the proper trading decision. For example, as the article points out, does "surprise" mean the price is going up or down? One solution to this is to make sure that algorithms properly combine pieces of information (e.g., matching "surprise" found in a news piece with relevant price data). Another solution is tagging the news items fed into the computers (but then how do you know whether the tags are correct?) -- which is being done by financial news providers like Dow Jones.

I believe that there are more profound and more troubling problems than those pointed out by the article. First, we might wind up seeing similar problems that we got with 'programed trading' (which is not necessarily the same as algorithmic trading) that contributed to the October 1987 crash that rocked the financial market; i.e., you can get massive herding effects since many algorithms (even if superficially different from one another) will often behave in similar ways to each other.

The second problem is that algorithmic trading is essentially data mining. Now, most people in the business world think that data mining is a good thing. In some cases, it can be on a practical level. But, to thinking mathematical statisticians, data mining is a highly dubious practice. As Nassim Taleb might put it, algorithmic trading might be mining data to confirm whatever 'white swans' that one hopes will provide trading profits. But what if a black swan happens? It's doubtful that trading algorithms can pick up on black swans any better than their human trading compatriots can.

A third problem -- which I won't elaborate on since there are mountains of books written on this subject -- are the ones faced by anyone working in artificial intelligence ... which algorithmic trading is sort of aiming at.

Mimicking Soros, Dangers of Low Volatility, & Collusion in the Music Industry

I've noticed several interesting articles in The Economist magazine recently that are worth noting.

In his/her column, Soros on the cheap (April 4, 2007), Buttonwood makes the case for currency trading as a valuable addition to an investment portfolio. In particular, currency trading based on exchange rate models that take into account the 'carry trade' (an arbitrage technique which is similar to short selling), momentum, and purchasing power parity, have shown themselves to be profitable.

In last week's column, Sting in the tail: Is low volatility making the world too complacent about risk? (April 12, 2007), Buttonwood makes an even more convincing argument that instruments, practices, and institutions in the financial markets that leads to a relatively low volatility environment most of the time may wind up increasing 'tail risk' (i.e., extreme risk). Low volatility may be a 'false dawn' -- or perhaps even a cruel joke being played by the forces of market randomness -- that lull investors and traders into a false sense of security ... suckering people along into making bad financial decisions until catastrophe strikes.

Finally, in the March 29, 2007, 'Economics Focus' column, Silent orchestration: Can record companies act in concert, even without agreeing to do so?, The Economist examines the possibility that there is tacit collusion in the music industry by applying the logic of game theory. This type of analysis is very important to monopolies, antitrust, and competition laws & regulations. As the column points out, it is difficult to hold a cartel together (tacit or explicit), but, using game theoretic methods, it isn't impossible.