Modelling Financial Time Series using Grammatical Evolution

author: Kamal Adamu, Center for Computational Finance and Economic Agents, University of Essex
published: Aug. 21, 2009,   recorded: July 2009,   views: 8292


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The traditional models of price, and its statistical signatures are often based on limiting assumptions, such as linearity. Moreover, the model developer is faced with the model selection problem, and model uncertainty. In this paper we introduce a method based on Grammatical Evolution (GE) to evolve models for predicting financial returns, and we examine the profitability of these models. Our empirical analysis demonstrates that for some securities our method is able to produce models of return that are lead to more profitable trading compared with an Autoregressive model picked using Aikake Information Criterion (AIC), under the assumption of frictionless markets.

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Reviews and comments:

Comment1 Kamal Adamu, August 22, 2009 at 10:36 a.m.:

regarding the question about multiplying the moving average with another variable (lagged return in this case), perhaps we should be examining "interaction effect"

Comment2 Anonymous, August 22, 2009 at 6:54 p.m.:

MashaAllah !!!! ;)

I think your project is really GOOD.

Comment3 Abdullah Adamu, August 22, 2009 at 7:04 p.m.:

MashaAllah Brother!!!

YOUR PROJECT ROCKS!!! It is really genius to simulate business behavior from what I understand using AI algorithm. I think it would reduce risks business have to have by alot.
- Thats from what I understand-

The best Algorithms are from studying creation itself, and this is an example of it ;):D


Comment4 Isa M. Basheer, February 15, 2010 at 2:41 p.m.:

Bro, am really proud of u! u made me feel wonderful, keep it up!!!

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