Modelling stock markets by probabilistic 1-d cellular automata

Hakman A. WAN

doi:10.1080/00207169408804323

Modelling stock markets by probabilistic 1-d cellular automata

Hakman A. WAN

Research output: Journal Publications › Journal Article (refereed) › peer-review

2 Citations (Scopus)

Abstract

The concept of probabilistic cellular automata is introduced in this paper. The automata are used to model a simple stock market in which the buying and selling of a stock is governed by a probabilistic transition function which is also a function of time. It is possible to apply theories of Markov chain, e.g. absorption time, to this situation. Some popular strategies of investing in a stock market can also be simulated by the cellular automaton models with appropriate transition functions.

Original language	English
Pages (from-to)	167-176
Number of pages	10
Journal	International Journal of Computer Mathematics
Volume	53
Issue number	3-4
DOIs	https://doi.org/10.1080/00207169408804323
Publication status	Published - 1 Jan 1994
Externally published	Yes

Keywords

Cellular automata
Correlation dimension
Markov chain
transition function

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10.1080/00207169408804323

Cite this

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AB - The concept of probabilistic cellular automata is introduced in this paper. The automata are used to model a simple stock market in which the buying and selling of a stock is governed by a probabilistic transition function which is also a function of time. It is possible to apply theories of Markov chain, e.g. absorption time, to this situation. Some popular strategies of investing in a stock market can also be simulated by the cellular automaton models with appropriate transition functions.

KW - Cellular automata

KW - Correlation dimension

KW - Markov chain

KW - transition function

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DO - 10.1080/00207169408804323

M3 - Journal Article (refereed)

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JO - International Journal of Computer Mathematics

JF - International Journal of Computer Mathematics

IS - 3-4

ER -

Modelling stock markets by probabilistic 1-d cellular automata

Abstract

Keywords

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