Information can cause one of three price reactions: up, down, or flat. Prices stay the same if the market accurately anticipated the information. Chances are, though, that the information will change prices. One thus knows that when an EIA report comes out, ceteris paribus, prices are more likely to spike or dive than stand still.
"Veteran" traders set up limit orders so that if prices rose they'd buy and if prices fell they'd sell. It's a momentum trade. Unfortunately, they were sloppy, submitting orders before the data came out. Arbitrageurs realised that these lazy limit orders could themselves be cannibalised by nudging the price around to see if it triggers any hidden orders prematurely. The order's premature execution would then create a tiny, temporary momentum effect that the arbitrageur could ride. This is called banging the beehive.
The trader pushed out was setting up information-less standing orders before the report released. The trader adding information to the market, e.g. through unique analysis, is not concerned by pre-release volatility.
It is possible, here, that a massive lazy limit was prematurely triggered and subsequently mis-interpreted. It's tough to say. With limited information I'd caution against Nanex's assumption of the low prior probability insider information hypothesis.
Basically the company was about to release the findings from a key clinical trial. The stock price would either soar or it would flop.
Tuesday 12pm * stock price is $25
Tuesday 12pm - 12:27pm * massive wave of selling starts the price of the stock dropping
Tuesday 12:27pm - exchange halts trading at $11.81
Mind you, at the exact same time as this was happening, the company was releasing very positive clinical trial data.
So what happened?
The guess is that some large trader did some investigating and found out that a lot of people had stop-loss orders on the stock. The trader slowly accumulated a large position, then rapidly sold off shares right before the announcement.
This caused the stock price to drop, which initiated a number of stop loss orders, which further eroded the stock price. Since it's a small company with a pretty small float, it doesn't take much volume to really swing the price.
The trader then bought back a number of shares (at a very reduced price).
"Information can cause one of three price reactions: up, down, or flat."
Then there is no cause. Prices change somewhat randomly whether there is some information or not. The only correct predictive model is something like " there is 80pc chance that the price will be between -10 and 30 of its current value". Anything else is reading in a crystal ball.
I didn't invent any of these, read Thinking Fast and Slow if you have any doubts.
By price reaction I meant deviation from the path without the information. I was qualitatively representing the ensemble, i.e. parametric space, of possible price movements on (-∞, ∞) by mapping them to up <- (-∞,0), down <- (0,∞), and flat <- 0.
Let Px(t) be the price at time t. Px(t+δ) given Px(t), i.e. P[Px(t+δ)|Px(t)] is D = Gaussian(Px[t], vol) in an efficient market. We know relevant information is going to be added to the market at t+δ, but we do not precisely know its content until t+δ. The market can, however, make an educated guess. This information is a kernel with a central tendency* and an entropy, i.e. second moment, H. The price at t+δ is now D' = Gaussian(Px[t], vol + Hω), where ω is the weight of the information. Since H and ω are both positive we can see that D' is platykurtic with regards to D. D'-D, the impact of knowing that salient information will hit the market at a certain time, looks like a short butterfly P&L.
Thanks for the book recommendation; I enjoy Kahneman's work.
*If ω were very low or H very high, e.g. a unitary distributed kernel would have infinite Shannon's entropy, you are correct in assuming that it would have no impact on D', i.e. D'-D would be zero.
"Veteran" traders set up limit orders so that if prices rose they'd buy and if prices fell they'd sell. It's a momentum trade. Unfortunately, they were sloppy, submitting orders before the data came out. Arbitrageurs realised that these lazy limit orders could themselves be cannibalised by nudging the price around to see if it triggers any hidden orders prematurely. The order's premature execution would then create a tiny, temporary momentum effect that the arbitrageur could ride. This is called banging the beehive.
The trader pushed out was setting up information-less standing orders before the report released. The trader adding information to the market, e.g. through unique analysis, is not concerned by pre-release volatility.
It is possible, here, that a massive lazy limit was prematurely triggered and subsequently mis-interpreted. It's tough to say. With limited information I'd caution against Nanex's assumption of the low prior probability insider information hypothesis.