Modelling stock markets by probabilistic 1-d cellular automata

Hakman A. WAN

    Research output: Journal PublicationsJournal Article (refereed)

    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 languageEnglish
    Pages (from-to)167-176
    Number of pages10
    JournalInternational Journal of Computer Mathematics
    Volume53
    Issue number3-4
    DOIs
    Publication statusPublished - 1 Jan 1994

    Fingerprint

    Cellular automata
    Stock Market
    Cellular Automata
    Modeling
    Probabilistic Cellular Automata
    Cellular Automaton Model
    Markov processes
    Automata
    Markov chain
    Sales
    Absorption
    Financial markets
    Model

    Keywords

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

    Cite this

    @article{e184bcca128146098910dbd825b94c24,
    title = "Modelling stock markets by probabilistic 1-d cellular automata",
    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.",
    keywords = "Cellular automata, Correlation dimension, Markov chain, transition function",
    author = "WAN, {Hakman A.}",
    year = "1994",
    month = "1",
    day = "1",
    doi = "10.1080/00207169408804323",
    language = "English",
    volume = "53",
    pages = "167--176",
    journal = "International Journal of Computer Mathematics",
    issn = "0020-7160",
    publisher = "Taylor and Francis Ltd.",
    number = "3-4",

    }

    Modelling stock markets by probabilistic 1-d cellular automata. / WAN, Hakman A.

    In: International Journal of Computer Mathematics, Vol. 53, No. 3-4, 01.01.1994, p. 167-176.

    Research output: Journal PublicationsJournal Article (refereed)

    TY - JOUR

    T1 - Modelling stock markets by probabilistic 1-d cellular automata

    AU - WAN, Hakman A.

    PY - 1994/1/1

    Y1 - 1994/1/1

    N2 - 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.

    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

    UR - http://commons.ln.edu.hk/sw_master/6974

    U2 - 10.1080/00207169408804323

    DO - 10.1080/00207169408804323

    M3 - Journal Article (refereed)

    VL - 53

    SP - 167

    EP - 176

    JO - International Journal of Computer Mathematics

    JF - International Journal of Computer Mathematics

    SN - 0020-7160

    IS - 3-4

    ER -