NetLogo User Community Models
by Carlos Pedro Gonçalves (Submitted: 11/04/2003)
WHAT IS IT?
This is a model of herding behaviour in a financial market. The market goes from disorganized states with
HOW IT WORKS
Patches are financial agents that at each time can either buy or sell an asset. The decision of each agent
HOW TO USE IT
There is one critical parameter that controls the herding behaviour, this is the degree of importance an agent gives to his neighbours (the coupling parameter). If the attention is high then we observe herding behaviour if it is small then there is a disorganized state.
A = (abs (sin (f)) * 0.03)
We can control the parameters of the random component, namely, the mean and standard deviation of z. These two parameters lead to different dynamics in terms of herding behaviour, for instance the higher the standard deviation the less the periodic component is felt. The higher the mean, the more the market tends towards an organized behaviour.
The user can also control the parameters of the random component of the signal that each agent receives specifically the standard deviation and the mean. This expresses the agents own opinion. If one increases the mean towards a positive value, there is a bias towards buying, if one increases the standard deviation the market stays in the disorganized state longer. The standard deviation of the own signal also affects stability of opinions.
THINGS TO NOTICE
Notice how the market passes from a disorganized state towards an organized state, and see what happens to the price.
THINGS TO TRY
Run the model once as is presented then explore the parameter space by changing first the parameters that control the herding behaviour, then the own signal and finally the noise traders. This order of exploration of parameter space provides important information concerning the influence of the various elements on the model.
EXTENDING THE MODEL
Possible extensions could be:
CREDITS AND REFERENCES
Sornette, Didier (2003), Critical Market Crashes, http://arXiv:cond-mat/0301543
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