Rise Of Black Box Trading May Pose Risk In Chaotic Markets

A protagonist of “black box”, or algorithmic, trading joked recently that there were three sorts. Aside from straight black box, where computers faster than mere humans trade on barely detectable differences between prices, there was “grey box” trading, where there was some human intervention. And there was “brown box” trading, which he defined as “black box trading with human intervention which has all gone wrong”.

One wonders whether he is still laughing. The past few years have seen the inexorable rise of algorithmic trading, which is largely behind the huge increase in volumes on world stock markets – the London Stock Exchange recently announced total monthly trades that had doubled year-on-year.

The computers trade using sophisticated algorithms drawn up by some of the most brilliant minds in mathematics. By one reckoning, a third of all trading conducted in Europe last year was algorithmic.

Algorithmic trading grew out of the increasingly computerised trading on world markets that began in the 1970s. What many of the programs do is spot tiny inefficiencies between prices in various categories, be they equities, derivatives or whatever. This is arbitraging, as happens anywhere in the market, but on a micro scale. Because profits of such transactions are minuscule, it is necessary to carry out millions of them, at speeds undetectable to the human mind. Meanwhile, the same sort of transactions, at similar volumes, are taking place in other funds and dealing rooms.

Such programs rely on reasonably orderly markets, where patterns detected in the past can be expected to be repeated. The suspicion is that, in the chaotic markets of last week, the normal patterns disintegrated. If the machines kept trading, the results could have been catastrophic.

Proponents of black box trading insist that there are plenty of risk-management programs and other fail-safes that prevent things from going awry. The experience of Goldman Sachs last week may suggest otherwise.

Pretty much everything the financial markets do is based upon the principals of the time value of money, or the risk/reward ratio. The software is only as good as the input data, and if the input data doesn't allow algorithms to correctly calculate risk, disasters can happen.

The software that runs financial institutions is very well written designed, and tested, for the obvious reasons that even the smallest bug or rounding error can lead to billions of dollars in losses. I would love to compare the quality of the software at Wall street to, say, global warming climate models. Do these climate models have enough input to accurately forcast climate enough to justify hundreds of billions of real dollars spent in order to reverse the effects of climate which the models claim will occur?

OK, m'friend, I'll break it down.

Delta is a measure of the sensitivity of an option's price to a 1-unit move in the underlying mkt, be it a futures mkt or a share price. If an option has a delta of, let us say, 0.6, this means that the price of the option (according to the model, typically some version of Black-Scholes or Cox-Ross-Rubenstein) will move about 6/10ths of a point when the underlying mkt moves 1 point.

If the trader is long the market, long the calls in a market or short the puts in a market, these delta figures are positive numbers. If the trader is short the market, long the puts in the market, or short the calls in the market, these delta figures are negative numbers. To find the delta of an entire position (presumably a position made up of both futures and options), the trader simply adds up the deltas of each position he's trading. A straight long position of 100 shares or 1 futures contract has, by definition, a delta of +1. Similarly, a straight short position has, also by definition, a delta of -1. Options' deltas typically range between 0 and 1. If this sum is a positive number, the trader is said to be delta-long, and conversely delta-short if the sum is negative.

Gamma is a second sensitivity of option price to a move in the underlying market; specifically, it is the partial 2nd derivative of price, and the partial 1st derivative of delta. In plain English, it is a measure of how fast delta will change given a 1-unit move in the price of the underlying.

Theta is a third sensitivity of option price, but has nothing to do with movement of the underlying market's price. It is a measure of the rate of decay in an option's price with regard to time.

So, if one is delta-long, gamma-long, and theta-short, it is understood (by traders, at least) that the trader has a position which notionally profits if A) the underlying market does nothing or not much prior to the expiration of the options, and B) will probably profit if the underlying market moves up prior to option expiration. This is a reasonable market stance in NG (natural gas) futures just now, because NG prices are notoriously affected by summer heat waves (duh!) and the advent of hurricanes (double duh!).

When one is short a market's puts, as I am in a couple of markets right now, one is effectively LONG the market...the delta figure is positive.

Futures, unlike (fapp) stocks, have expirations of their contracts in various months of the year. Thus, in the V/Z hog spread, I am presently long the V contract (October) and short the Z contract (December), expecting the price spread between the two contracts to widen in favour of October.

That should be a pretty good translation for you. If you have other questions, please do ask.

Good trading, and FReegards!

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