[R-SIG-Finance] statistical features of equity time series

alexios ghalanos alexios at 4dscape.com
Sun Oct 28 15:36:58 CET 2012

You might find an agent based modelling approach useful - one 
interesting implementation of which can be found here:

On 28/10/12 16:24, Alex Grund wrote:
> Hi Matthew,
> 2012/10/28 Matthew Gilbert <matthew.douglas.gilbert at gmail.com>:
>> The books "Analysis of Financial Time Series" by Ruey Tsay and "Statistics
>> of Financial Markets" by Franke, Hardle and Hafner are both good references.
> Thank your for this hints!
>> But ultimately if the end goal is to test a trading strategy why simulate
>> your own data? It seems like a lot of work and the end result would be to
>> generate a profitable strategy on fictitious data?
> No, the goal should NOT be to have a trading strategy. The goal is to
> find some rational bahaviors.
> For example: Given special characteristics of prcing data, is it
> rational to invest 300000 $ directly or to invest 100000 $ at each
> month's first trading day for three month. What will the result likely
> be in 12 months?
> Is it rational to take some profits?
> ...
> That is not the same as a strategy "buy if MA crosses price" or
> something like that. It is rather an market condition independent
> bahavior. If one cannot "predict" the market, is it possible to reduce
> risk or gain extra returns if one does other things like buy and hold,
> but not with any information influence, only by bhavioral patterns.
> That's why I called it "bahavior" rather than "strategy".
> Why not on live data?
> I could run simulations on 500 stocks (e.g. from SP500). But to
> eliminate survivorship bias etc. and to run much more tests (1000s to
> 10000s) it sounds more suitable to run against artificial market data.
> Maybe special characteristics are revealed which gives an insight to
> "black swans" which are not obvious from real data.
> --a
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