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Comparison of algorithmic trading using the homogeneous and non-homogeneous Markov chain analysis

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23510%2F17%3A43932496" target="_blank" >RIV/49777513:23510/17:43932496 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Comparison of algorithmic trading using the homogeneous and non-homogeneous Markov chain analysis

  • Original language description

    This empirical study deals with stochastic modelling of a short-term share price development. We use Markov chain analysis (MCA) to predict the share price development. When defining a state space we assume that the share price moves in three types of trends: primary, secondary and minor. The subject of our interest is a minor trend, which usually lasts for several days. During this trend the share price accumulates a certain profit or loss in relation to the price at the beginning of the trend. The state space is defined by the amount of the accumulated profit or loss. The aim of this study is to compare two approaches to modelling the state space. In the first approach, we assume homogeneous Markov chains, i.e. approximately the same volatility, and MCA is performed with unvarying state space. In the second approach, we assume non-homogeneous Markov chains, i.e. a changing volatility, and MCA is performed with varying state space. We create trading strategies for automatic generation of buying and selling orders based on these models. Three business systems have been created for each approach. The profitability of each business system is calculated and compared. The study was performed using historical daily prices (opening and closing) of CEZ shares from the beginning of 2006 until the end of 2016. This study has proved that trading models with varying state space, on the average, outperform trading models with unvarying state space.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2017

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    PROCEEDINGS OF THE 14TH INTERNATIONAL SCIENTIFIC CONFERENCE PART 2 : EUROPEAN FINANCIAL SYSTEM 2017:

  • ISBN

    978-80-210-8609-8

  • ISSN

  • e-ISSN

    neuvedeno

  • Number of pages

    9

  • Pages from-to

    223-231

  • Publisher name

    Masarykova univerzita

  • Place of publication

    Brno

  • Event location

    Brno

  • Event date

    Jun 26, 2017

  • Type of event by nationality

    EUR - Evropská akce

  • UT code for WoS article

    000418110800027