Conference Paper

Testing the Dinosaur Hypothesis under Empirical Datasets

DOI: 10.1007/978-3-642-15871-1_21 Conference: Parallel Problem Solving from Nature - PPSN XI, 11th International Conference, Kraków, Poland, September 11-15, 2010. Proceedings, Part II
Source: DBLP


In this paper we present the Dinosaur Hypothesis, which states that the behaviour of a market never settles down and that
the population of predictors continually co-evolves with this market. To the best of our knowledge, this observation has only
been made and tested under artificial datasets, but not with real data. In this work, we attempt to formalize this hypothesis
by presenting its main constituents. We also test it with empirical data, under 10 international datasets. Results show that
for the majority of the datasets the Dinosaur Hypothesis is not supported.

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    • "EDDIE 7 should be viewed as an extension of the simple GP presented in [4], in the sense that their only difference is in the fitness function, which now is a constrained one. "
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    ABSTRACT: The Dinosaur Hypothesis states that the behaviour of a market never settles down and that the population of predictors continually co-evolves with this market. This observation had been made and tested under artificial datasets. Recently, we formalized this hypothesis and also tested it under 10 empirical datasets. The tests were based on a GP system. However, it could be argued that results are dependent on the GP algorithm. In this paper, we test the Dinosaur Hypothesis under two different GP algorithms, in order to prove that the previous results are rigorous and are not sensitive to the choice of GP. We thus test again the hypothesis under the same 10 empirical datasets. Results are consistent among all three algorithms and thus suggest that market behavior can actually repeat itself, and have a number of `typical states', where past rules may become useful again.
    Computational Intelligence (UKCI), 2010 UK Workshop on; 10/2010
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    ABSTRACT: This paper extends a previous market microstructure model, which investigated fraction dynamics of trading strategies. Our model consisted of two parts: Genetic Programming, which acted as an inference engine for trading rules, and Self-Organizing Maps (SOM), which was used for clustering the above rules into trading strategy types. However, for the purposes of the experiments of our previous work, we needed to make the assumption that SOM maps, and thus strategy types, remained the same over time. Nevertheless, this assumption could be considered as strict, and even unrealistic. In this paper, we relax this assumption. This offers a significant extension to our model, because it makes it more realistic. In addition, this extension allows us to investigate the dynamics of market behavior. We are interested in examining whether financial markets’ behavior is non-stationary, because this implies that strategies from the past cannot be applied to future time periods, unless they have co-evolved with the market. The results on an empirical financial market show that its behavior constantly changes; thus, agents’ strategies need to continuously adapt to the changes taking place in the market, in order to remain effective.
    Applications of Evolutionary Computation - EvoApplications 2011: EvoCOMNET, EvoFIN, EvoHOT, EvoMUSART, EvoSTIM, and EvoTRANSLOG, Torino, Italy, April 27-29, 2011, Proceedings, Part II; 01/2011
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