Modelling stock markets by probabilistic 1-d cellular automata

Hakman A. WAN

Research output: Journal PublicationsJournal Article (refereed)peer-review

2 Citations (Scopus)


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 languageEnglish
Pages (from-to)167-176
Number of pages10
JournalInternational Journal of Computer Mathematics
Issue number3-4
Publication statusPublished - 1 Jan 1994


  • Cellular automata
  • Correlation dimension
  • Markov chain
  • transition function


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