Jim Brown offered these details on the oil trading:
Those funds interviewed said the massive amount of stop losses that were triggered was beyond comprehension…. When the crash finally came the number of positions liquidated was staggering. As each technical level was broken it triggered more stop losses and more short selling to capture the drop….
Credit Suisse analysts said the high frequency and algorithmic trading accounted for about half of all the volume in the oil markets.
Every day, futures prices can and do move in response to how many people want to buy or sell the contracts. As I explained in a recent study in Brookings Papers on Economic Activity, inventory arbitrage forces the spot price to move along with the futures price. But as I also explained there, this does not mean that sentiment or speculation alone can put the price of oil at any arbitrary value. Ultimately, the critical question is whether the spot price is one at which the physical quantity produced is equal to the physical quantity consumed. Whether today’s price indeed accomplishes this was the focus of my discussion of these events last weekend.
But clearly, there are lots of big traders out there who are thinking not along these lines but rather in terms of Keynes’ beauty contest, supposing that all that matters is guessing what other people think the price will be, and fooling themselves into believing that by programming their computers to buy high and sell low they are going to outsmart the other players.
Count me among those who maintain that buying high and selling low is unlikely to be a successful trading strategy. But if enough people believe otherwise, they can wreak a bit of havoc on the rest of us before they themselves go belly up.
This post originally appeared at Econbrowser and is reproduced here with permission.
One Response to “Oil Prices and Trading Mechanics”
At the March IMF conference on “Macro and Growth Policies in the Wake of the Crisis” one of the panel participants (I forget which panel) said the he had just found out, to his surprise, that traders were developing predator algorithms design to identify trade algorithms, learn their trigger rules, then exploit them. I filed it as Quants 2.0. I was expecting you to go in that direction.