Hi Jay, found the double secret explanation not obvious before, to the believers who invest in technology without having ever stepped into a technology lab, with all the complicated boxes hooked together. You mentioned the complications in the post this is responding to...
Message 15813707
I remember one time at school, you were not listening to the professor talking at during the power electrical lab ... until you vaguely heard him tell the rest of the lab what not to do "...do not hold plugs A and B in your hands, because, even when not powered, the residual energy stored in the coil can seriously kill you ...".
Remember how you got his attention ("eh? professor, what do I do now?"), with plug A in one hand and B in another, about arms length away from death, literally.
Now we have folks playing the markets, without understanding the true nature of the markets and money, maybe months away from financial death.
You do not understand the markets either, but you do understand fear, not the paralyzing sort, but the gene pool enhancing sort (you check in with darwinawards.com often enough).
I also remember the time you used a mixing stick to stir the mystery powdery content of the chemistry lab, aiming eventually to identify the composition, over aperiod of weeks of analysis. You screamed as the super heated glass beaker bottom seemed to be burning through. The class stared at you, and the teacher showed obvious alarm. Then the true explanation was evident. Instead of a metal mixer, you used a plastic one.
Never did successfully identify the powder, given all the little plastic beads mixed in.
So many explanations possible for any one observation. Better to triangulate.
economist.com
QUOTE
Patterns in financial markets
Predicting the unpredictable
May 31st 2001
From The Economist print edition
Some physicists think they can see into the future of markets
STOCKMARKET crashes can be like buses: you wait ages for one to come along, then three arrive at once. That has not stopped people from trying to understand why they happen, of course. But such understanding is handicapped by two things. The first is that markets are complex systems that are hard to analyse. The second is a widespread assumption that, over three centuries of stockmarket trading as we know it, any patterns that do predict the future will already have been identified and “arbitraged” out of existence by people taking advantage of them.
David Lamper and his colleagues at Oxford University disagree. As physicists and mathematicians, they frequently find themselves dealing with complex natural systems, and in a paper submitted to Physical Review Letters, they apply that experience to the artificial world of market mechanisms. They suggest that price patterns do exist that have not been arbitraged out. These patterns are created by the collective actions of market traders themselves—and could be used to predict sudden falls in a market.
Dr Lamper’s work starts from the observation that extreme price movements in financial markets happen far more often than would be expected by chance. Presumably, therefore, there is some reason behind them. To explore what that might be, he and his colleagues built a computer model which attempts to emulate market behaviour by running a large number of software “agents” at the same time. These agents “trade” with each other according to certain strategies. (Similar trading strategies are used in the real world to carry out automatic share-dealing.)
Each agent has the same information about the past, and is fed new price data at regular intervals. But, rather like human traders, agents differ from one another in how they interpret this information. Some, for example, will look at a past pattern of rising prices, and assume that the market will continue to skyrocket. Such bulls will want to buy. Other agents will consider a rising market to present a good opportunity to sell. Still others will want to buy in a falling market that has been rising until recently. And so on.
The resulting computer-based market produces price features that are statistically similar to those of a real one, which suggests that the team’s assumptions about how traders work are accurate. Unlike a real market, though, a computer market can be run backwards—that is, it can be fed a set of market data and told to work out what trading strategies are likely to have produced them. If the strategies employed before a sudden downturn are analysed, the result is intriguing: the agents have generally come to agree with one another about the best way to trade.
Although standard financial-market theory assumes that it is impossible to tell anything about the future direction of a market by looking at what has happened in the past—so that markets follow a “random walk”—most investors are seeking to do just that. When both real market data and random simulations of such data are put before traders, they claim to see patterns. This is no surprise. People see patterns, where there are none, all the time—in clouds, in lotteries, in mountains on the surface of Mars, and even in root vegetables. Predicting a number in a lottery based on such a spurious pattern cannot, of course, have any effect on whether that number is actually drawn. But the same is not necessarily true of the stockmarket. When many traders think they see the same pattern, they may respond in the same way. They may thus, collectively, create a real pattern.
Lemmings over the cliff
And not only real, but unstable. For, paradoxically, the more orderly a market appears, the less stable it is. (Think of dominoes up-ended on a table. If they are distributed at random, knocking one over causes little damage. If they are in a line, the whole lot will come down.)
It is the changes in strategy that lead to this orderly instability which the researchers say they can detect. When their system finds many traders crowding into one type of strategy, they know that a large downward change may be imminent. The super-strategy to beat this is obviously to sell first or even, for the brave, to go short.
So much for theory. The question is: are the researchers rich yet? Well, no. But the Oxford model has been tested against the real world, and it looks as though it may work surprisingly well.
The first test was against minute-by-minute historical data from a foreign-exchange market: a London bank’s dollar-yen market between 1990 and 1999. The model detected enough selling and buying opportunities during this nine-year period to yield a 380% profit.
Having tried out the principle in the relative simplicity of a currency market, Dr Lamper is now taking on Nasdaq, the tech-heavy stockmarket whose tumble in April 2000 signalled the end of the dotcom boom. Running Nasdaq data through the Oxford model suggests that there was, indeed, a period of increased predictability in the six months before April 2000, compared with the months before that.
None of which means that the model will work for the future, any more than many previous such efforts. Being canny, the researchers are anyway keeping details of the algorithm they use to generate their results under wraps until the patent they have applied for is locked up. Nor are they too worried if the details of their super-strategy leak out, since they think they could detect its use in the market. That would allow them to play a “super-super-strategy” that made use of the knowledge. On that, they are keeping super-quiet.
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