I write about the issue of power, and lo I then discover a story about the 147 companies that control everything. An interesting read about modelling of networks really.
These are mostly financial houses as per my note about corporate power.
A retort tried to argue that these finance companies were ultimately just agents for all of us as investors - but that is technically rubbish, especially if what we "own" is an investment in a super fund.
Meanwhile in another observation on complexity economics we learn 'Groupon Is A Disaster'. Anyone who lived through the tech boom and bust of the 1990s could have told you that.
That is about as un-newsworthy as is The Internet is Insecure. Problem with that report is the assertion "Australia is taking a positive lead by working with other nations to identify and try to solve some of the issues with the internet. But the pace of this world-wide effort is glacial and more needs to be done." Stuffed if I can see that happening given the very low Australian presence at ISOC and IETF meetings.
Novae Meridianae Demetae Dexter delenda est
Showing posts with label complexity. Show all posts
Showing posts with label complexity. Show all posts
Wednesday, October 26, 2011
Monday, October 10, 2011
High speed and computerised trading
Interesting item today by a sociologist on The Conversation about the danger of high-speed trades in stock markets.
It contrasts with other reports of studies that conclude;
Economic research thus far provides no direct evidence that high frequency computer based trading has increased volatility.
Of course, the absence of evidence that high-speed trading increases volatility does not mean that it high-speed trading doesn't increase volatility. But the bigger question isn't just what happens to volatility but how markets are affected.
The particular issue is how a trader, automated or otherwise, values a stock. Like any asset there are two sources of future value for the stock. The first is the income stream from it represented by the value of dividends and an appropriate (see note) discounting formula. The second is the likely future price realised when the asset is sold.
The difficulty in real world markets is how much weight should be given to each. More specifically what is the right response for a trader to make as asset prices move without any corresponding change in expectation of future earnings? If prices are increasing do you buy (on the expectation of future increases) or sell (as the price is now higher than the value of future earnings).
Accountants don't help much because they have accounting rules about assets being "marked to market" - which was partly the issue in the GFC as finance houses reported massive profits by revaluing market assets despite the fundamentals not being there.
The second issue is the extent of correlation between asset prices. The GFC in part occurred because of an assumption that not all house prices would decline at once - but they did. That invalidated the risk modelling that underpinned the derivatives.
The same is true of stockmarkets. Fear of a recession will drive down all stock prices because the fundamentals change - recession equals lower profits. But that small shift based on fundamentals then feeds into the future price expectation.
My sense is that speedier automated trading doesn't increase volatility as such. But standard market models tend to ignore the kinds of real world distributions identified by Talebi in Black Swan or Mandelbrot in the Mis-behavior of Markets.
The other unanswered question is whether once markets have sunk into a hole high-speed automated trading makes it harder to climb out of them.
These are issues that I think are better resolved not by statistical analysis of market behaviour but by agent-based modelling of markets. The latter would provide the means to experiment with market rules designed to manage pricing better. An example of a possible rule is to limit the use of "mark to market" in accounting - that assets cannot be re-valued up faster than a rate of double CPI purely on the basis of "mark to market", and that equally they must be re-valued down strictly according to "mark to market." I don't know how well that rule would deal with the issue. That's why simulation would be desirable.
Note: Discounting is used by economists as if it is easy. However, the first thing we know is that real traders apply "hyperbolic" discounting - they overweight short term payments. The second is that the appropriate "discount rate" is really the market price of another asset - Government bonds - plus other unknown risk factors. In other words "discounting" sounds like a scientifically sound procedural method but it is as much gueework (or estimation) as science.
Novae Meridianae Demetae Dexter delenda est
It contrasts with other reports of studies that conclude;
Economic research thus far provides no direct evidence that high frequency computer based trading has increased volatility.
Of course, the absence of evidence that high-speed trading increases volatility does not mean that it high-speed trading doesn't increase volatility. But the bigger question isn't just what happens to volatility but how markets are affected.
The particular issue is how a trader, automated or otherwise, values a stock. Like any asset there are two sources of future value for the stock. The first is the income stream from it represented by the value of dividends and an appropriate (see note) discounting formula. The second is the likely future price realised when the asset is sold.
The difficulty in real world markets is how much weight should be given to each. More specifically what is the right response for a trader to make as asset prices move without any corresponding change in expectation of future earnings? If prices are increasing do you buy (on the expectation of future increases) or sell (as the price is now higher than the value of future earnings).
Accountants don't help much because they have accounting rules about assets being "marked to market" - which was partly the issue in the GFC as finance houses reported massive profits by revaluing market assets despite the fundamentals not being there.
The second issue is the extent of correlation between asset prices. The GFC in part occurred because of an assumption that not all house prices would decline at once - but they did. That invalidated the risk modelling that underpinned the derivatives.
The same is true of stockmarkets. Fear of a recession will drive down all stock prices because the fundamentals change - recession equals lower profits. But that small shift based on fundamentals then feeds into the future price expectation.
My sense is that speedier automated trading doesn't increase volatility as such. But standard market models tend to ignore the kinds of real world distributions identified by Talebi in Black Swan or Mandelbrot in the Mis-behavior of Markets.
The other unanswered question is whether once markets have sunk into a hole high-speed automated trading makes it harder to climb out of them.
These are issues that I think are better resolved not by statistical analysis of market behaviour but by agent-based modelling of markets. The latter would provide the means to experiment with market rules designed to manage pricing better. An example of a possible rule is to limit the use of "mark to market" in accounting - that assets cannot be re-valued up faster than a rate of double CPI purely on the basis of "mark to market", and that equally they must be re-valued down strictly according to "mark to market." I don't know how well that rule would deal with the issue. That's why simulation would be desirable.
Note: Discounting is used by economists as if it is easy. However, the first thing we know is that real traders apply "hyperbolic" discounting - they overweight short term payments. The second is that the appropriate "discount rate" is really the market price of another asset - Government bonds - plus other unknown risk factors. In other words "discounting" sounds like a scientifically sound procedural method but it is as much gueework (or estimation) as science.
Novae Meridianae Demetae Dexter delenda est
Wednesday, August 24, 2011
Top read of the day
This column from Wired is about the complexity of the structure of a crushed piece of paper and why the ball that results which is 90% air is so rigid.
For those economists out there still wedded to economic models derived from 19th century thermodynamics and electromagnetism, you might like to reflect on the thought that real world markets are much like the crumpled paper - apparently simple but really very complex.
The problem is we don't have X-ray microtomography to look at the structure of economic markets.
Novae Meridianae Demetae Dexter delenda est
For those economists out there still wedded to economic models derived from 19th century thermodynamics and electromagnetism, you might like to reflect on the thought that real world markets are much like the crumpled paper - apparently simple but really very complex.
The problem is we don't have X-ray microtomography to look at the structure of economic markets.
Novae Meridianae Demetae Dexter delenda est
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