Mr. Soros in his books talked about principles which are not used by today's financial mathematics — namely reflexivity of all actions on the market. Simply it can be given by following: expectations of traders are based on the news and historical prices. They trade based on their expectations and hence influence prices which then influence expectations on the next "turn". I have never met an implementation of these ideas in the scientific framework. Have you? If yes, please give me a reference.
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Keynes introduced this idea in the notion of a Keynesian Beauty contest: http://en.wikipedia.org/wiki/Keynesian_beauty_contest Anyone who uses a rolling window regression where the parameters and/or parameter estimates are re-fitted periodically are implicitly accounting for this reflexivity (i.e. the market's changing behavior as agents respond and adapt to each others actions) With respect to a scientific-framework, agent-based modeling is something that fits the bill and also game theory: http://en.wikipedia.org/wiki/Agent-based_model |
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The answers above are good, but I suspect they will be unsatisfactory if you are looking for implementations that are successful in practice. The sort of bottom-up analysis championed by Soros is very difficult to carry out in a rigorous, quantitative manner. This is true very generally, not just in finance. There are certainly models of financial markets that explicitly consider beliefs and actions of individual market participants (see game theory), but these always rely on extreme oversimplifications of actual human cognition and behavior. On the other hand, many seem to be able to develop a good intuitive sense for what sort of beliefs and biases are driving the behavior of financial market participants and exploit this knowledge. Soros is often counted among their ranks. Although this approach may be sophisticated, it is usually not considered quantitative. Soros often claims that he looks for a sort of "turning point" where market participants realize the flaws in their beliefs. This is in contrast with most quantitative models that implicitly assume that whatever structure is observed in the data will persist long enough for a profit to be extracted. |
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Sure it is used in non-linear dynamics. What is not used is that this non-linearity is caused by huge operators who cause the deviation from Normal Law delaying the reversion to the mean. This is just common sense and mathematically logics when you go straight to Normal Law premisces: all samples must have about the same weight and being independant. Clearly these premisces are hugely violated in Stock Market. |
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