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|Title:||The Market Fraction Hypothesis under Different GP Algorithms|
|Authors:||Kampouridis, Michael;Chen, Shu-Heng;Tsang, Edward|
|Issue Date:||2014-04-15 16:28:03 (UTC+8)|
|Abstract:||In a previous work, inspired by observations made in many agent-based financial models, we formulated|
and presented the Market Fraction Hypothesis, which basically predicts a short duration for any dominant
type of agents, but then a uniform distribution over all types in the long run. We then proposed a two-step
approach, a rule-inference step, and a rule-clustering step, to test this hypothesis. We employed genetic
programming as the rule inference engine, and applied self-organizing maps to cluster the inferred rules.
We then ran tests for 10 international markets and provided a general examination of the plausibility
of the hypothesis. However, because of the fact that the tests took place under a GP system, it could be
argued that these results are dependent on the nature of the GP algorithm. This chapter thus serves as
an extension to our previous work. We test the Market Fraction Hypothesis under two new 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 that were used in our
previous experiments. Our work shows that certain parts of the hypothesis are indeed sensitive on the
algorithm. Nevertheless, this sensitivity does not apply to all aspects of our tests. This therefore allows
us to conclude that our previously derived results are rigorous and can thus be generalized.
|Relation:||Information Systems for Global Financial Markets: Emerging Developments and Effects, Chapter 3, pp.37-54|
IGI Global, 2011
|Appears in Collections:||[經濟學系] 專書/專書篇章|
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