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

[Alex Grund]

looks nice, thank you very much for the link, I'll have a more
detailled look soon and will come back with my thoughts on this. --a

2012/10/28 alexios ghalanos <alexios at 4dscape.com>:
> You might find an agent based modelling approach useful - one interesting
> implementation of which can be found here:
> http://fimas.sourceforge.net/project_info.htm
>
> -Alexios
>
> 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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>