Quoting Frank Wimberly <[EMAIL PROTECTED]>: > What about the following issue: I suspect that a simulation > (agent-based or otherwise) of the "stock market" (e.g. the DJIA) could > be developed which is statistically indistinguishable from the real > thing. That is, the moments, fat tails, etc. would not serve to allow a > statistician to distinguish between the simulated signal and some, as > yet unobserved, actual data. Yet, such a simulation would have no > predictive value except on some set of measure zero. However similar to > the real world the simulation is, it won't tell you the level of the > DJIA next Tuesday.
One way is to get NYSE and NASDAQ datasets and iterate the 30 DJIA order books forward in time from a long period of real observed order flow. For each trader on each stock you could fit or engineer an agent that reproduces that order flow to some degree of precision (based on the evolving orderbooks they had in front of the and perhaps based on estimates of their initial inventory (or at least aversions to large changes). Now take all of the order books and iterate them forward in time with the synthetic agent rules until next Tuesday. You'd have estimates of all the best prices on all of the stocks and thus the DJIA, given assumption that the behaviors at work in the data would continue to occur into the future. One question is how intelligent do the agents have to be? That simple agents who do little more than hide their inventory can do a good job of getting aggregate statistics of the market right suggest that internal dynamics of the markets are as important as real news. http://www.santafe.edu/~jdf/papers/zero.pdf http://www.santafe.edu/~jdf/papers/TheoryForLongMemory.pdf http://www.santafe.edu/~jdf/papers/quantitativemodel.pdf ============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org
