Also valid will be insider trading on geopolitical news. Imagine all the billions of dollars shifting because of a speech or action taken by politicians.
Trading will be dead in next 10-20 years
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Great answer, not sure how anybody could argue against this.
Do you realize that pit trading and open outcry are the same thing?
The competition would be algo vs algo, and the variables fed in by individual traders and institutions. It's human nature to compete, otherwise we'll all be on the same side of the trade. Greed will set in and the bets will follow the money on the other side. Let the games begin
On the professional side...its algo vs algo right now. They've been competing against each other for many years now. Yet, I'm not sure where the retail algorithm trader is at...they competitive or too far away from the competition ?
P.S. A few retail algorithm traders here at ET say they are doing well.
Scientific American
https://www.scientificamerican.com/article/can-math-beat-financial-markets/
Can mathematical models beat markets?
They haven't yet. Science is about empirical fact. There is no question that optimistic people think they can beat the market, but they don't do it consistently with mathematical models. No model can consistently predict the future. It can't possibly be.
So what can math predict?
What you can do is predict the risk of a given event. The risk just means the chance that something bad will happen, for example. That you can do with increasing accuracy because we have more and more data. It's like insurance companies: they cannot tell you when you are going to die, but they can predict the risk that you will die given the right information. You can do the same thing with stocks. If you lose less, you get ahead of those who lose more.
Why do economists and "quants"—those who use quantitative analysis to make financial trades—have such faith in their mathematical models then?
If they're just to reduce risk, then they're very valuable. If you're worried, for example, about the segment of the Chinese economy that deals with steel, you make a model of what that whole market is all about and then you see if we did this what would likely happen. They're right some of the time. It's better than nothing.
But when they have excessive faith in these models, it's not justified. Math starts with assumptions; the real world does not work that way. Economics, which calls itself a science, too often doesn't start with looking at empirical facts in any great detail. Fifteen years ago even the idea of looking at huge amounts of data did not exist. With a limited amount of data, the chance of a rare event is very low, which gave some economists a false sense of security that long-tail events did not exist.
Why do you argue that financial markets are ruled not by Gaussian functions but by power laws—relations in which the frequency of one event varies as a power of some attribute of that event and are generally more L-shape than bell shape?
For anything that is random and fluctuating, like a financial market, a Gaussian function is a wonderful way to make a histogram of the outcome. If the things that fluctuate are not correlated at all with one another, then it's demonstrable that a Gaussian function is the correct histogram.
The catch is: in a financial market, everything is correlated. The proof of that is that if the stock market were Gaussian, then you'd never have a flash crash. A Gaussian crash would be an event that goes out to maybe five standard deviations [that is, a rarity on par with one part in two million]. In markets, this is simply not true. There are events that are 100 standard deviations. Every economist knows for sure that these rare events occur and cannot be described by a Gaussian function. The question is: What are you going to do about it?
Power laws are simply way more accurate. If you don't know the risk, you are not going to make the right decision, and the economy is at risk from these big fluctuations. It's no surprise when they come. The only reason you have to wait awhile is because they are rare. Knowing that they will happen forces anyone prudent to have a plan for what to do if it happens.
The idea that it would be a power law that describes all the events, the tails and the middle is really a major contribution. It allows one to quantify risk. You can read off a plot of the law the numerical chance for a downturn of any given size. It's very small for something that is 100 standard deviations out but not so small for something that is 10 standard deviations out. In fact, the S&P 500 fluctuations—which if they were Gaussian, would pretty much be constrained to plus or minus five standard deviations—you find, in a 10-year period, the number of events that exceed five standard deviations is not just one, it's 64. And the number that exceeds 10 standard deviations is eight, and there was one event that exceeded 20 standard deviations. It looks like a power law, and that's what it is demonstrated to be when every trade of every stock is analyzed.
There are an awful lot of rare events and they're all ignored. This is not the best way I want my retirement funds invested.
https://www.scientificamerican.com/article/can-math-beat-financial-markets/
Can mathematical models beat markets?
They haven't yet. Science is about empirical fact. There is no question that optimistic people think they can beat the market, but they don't do it consistently with mathematical models. No model can consistently predict the future. It can't possibly be.
So what can math predict?
What you can do is predict the risk of a given event. The risk just means the chance that something bad will happen, for example. That you can do with increasing accuracy because we have more and more data. It's like insurance companies: they cannot tell you when you are going to die, but they can predict the risk that you will die given the right information. You can do the same thing with stocks. If you lose less, you get ahead of those who lose more.
Why do economists and "quants"—those who use quantitative analysis to make financial trades—have such faith in their mathematical models then?
If they're just to reduce risk, then they're very valuable. If you're worried, for example, about the segment of the Chinese economy that deals with steel, you make a model of what that whole market is all about and then you see if we did this what would likely happen. They're right some of the time. It's better than nothing.
But when they have excessive faith in these models, it's not justified. Math starts with assumptions; the real world does not work that way. Economics, which calls itself a science, too often doesn't start with looking at empirical facts in any great detail. Fifteen years ago even the idea of looking at huge amounts of data did not exist. With a limited amount of data, the chance of a rare event is very low, which gave some economists a false sense of security that long-tail events did not exist.
Why do you argue that financial markets are ruled not by Gaussian functions but by power laws—relations in which the frequency of one event varies as a power of some attribute of that event and are generally more L-shape than bell shape?
For anything that is random and fluctuating, like a financial market, a Gaussian function is a wonderful way to make a histogram of the outcome. If the things that fluctuate are not correlated at all with one another, then it's demonstrable that a Gaussian function is the correct histogram.
The catch is: in a financial market, everything is correlated. The proof of that is that if the stock market were Gaussian, then you'd never have a flash crash. A Gaussian crash would be an event that goes out to maybe five standard deviations [that is, a rarity on par with one part in two million]. In markets, this is simply not true. There are events that are 100 standard deviations. Every economist knows for sure that these rare events occur and cannot be described by a Gaussian function. The question is: What are you going to do about it?
Power laws are simply way more accurate. If you don't know the risk, you are not going to make the right decision, and the economy is at risk from these big fluctuations. It's no surprise when they come. The only reason you have to wait awhile is because they are rare. Knowing that they will happen forces anyone prudent to have a plan for what to do if it happens.
The idea that it would be a power law that describes all the events, the tails and the middle is really a major contribution. It allows one to quantify risk. You can read off a plot of the law the numerical chance for a downturn of any given size. It's very small for something that is 100 standard deviations out but not so small for something that is 10 standard deviations out. In fact, the S&P 500 fluctuations—which if they were Gaussian, would pretty much be constrained to plus or minus five standard deviations—you find, in a 10-year period, the number of events that exceed five standard deviations is not just one, it's 64. And the number that exceeds 10 standard deviations is eight, and there was one event that exceeded 20 standard deviations. It looks like a power law, and that's what it is demonstrated to be when every trade of every stock is analyzed.
There are an awful lot of rare events and they're all ignored. This is not the best way I want my retirement funds invested.
Two different endeavors.
If that's where you're at, I don't have anything to add to this discussion.
That's what people who prognosticate doom and gloom over technology-driven trading don't understand. The behaviors of supply and demand in markets will still show themselves regardless of who's doing the trading. I've analyzed charts from as far back as the year 1200 and markets behaved the exact same way as they do now.
Glad to find that there are at least two of us in the world who realize this!