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Statements

Subject Item
n2:RIV%2F49777513%3A23510%2F13%3A43919711%21RIV14-MSM-23510___
rdf:type
n8:Vysledek skos:Concept
dcterms:description
The present paper concerns with prolongation of our study aims to predict the stock index trend of various stock indices using Markov chain analysis (MCA). The prediction of the trend using MCA is done using short, medium and long-term data. Each downloaded data is divided into two periods. The first one is used for estimating the trend, whereas the second one is used for comparison and evaluation, too. In the basic framework, we may use either homogeneous MC or nonhomogeneous one. When building model we are focused both on various state space discretizations and corresponding construction of transition probability matrices. Non-homogeneous matrices are constructed by linear interpolants between two transition probability matrices at given time steps. These objects represent a core of any MCA and its application. The results of the trend prediction using both versions of MCA are compared. Numerical calculations and computer implementations have been done by Excel and Mathematica modules, which are briefly discussed as well. The present paper concerns with prolongation of our study aims to predict the stock index trend of various stock indices using Markov chain analysis (MCA). The prediction of the trend using MCA is done using short, medium and long-term data. Each downloaded data is divided into two periods. The first one is used for estimating the trend, whereas the second one is used for comparison and evaluation, too. In the basic framework, we may use either homogeneous MC or nonhomogeneous one. When building model we are focused both on various state space discretizations and corresponding construction of transition probability matrices. Non-homogeneous matrices are constructed by linear interpolants between two transition probability matrices at given time steps. These objects represent a core of any MCA and its application. The results of the trend prediction using both versions of MCA are compared. Numerical calculations and computer implementations have been done by Excel and Mathematica modules, which are briefly discussed as well.
dcterms:title
Application of non-homogeneous Markov chain analysis to trend prediction of stock indices Application of non-homogeneous Markov chain analysis to trend prediction of stock indices
skos:prefLabel
Application of non-homogeneous Markov chain analysis to trend prediction of stock indices Application of non-homogeneous Markov chain analysis to trend prediction of stock indices
skos:notation
RIV/49777513:23510/13:43919711!RIV14-MSM-23510___
n8:predkladatel
n11:orjk%3A23510
n3:aktivita
n15:S
n3:aktivity
S
n3:dodaniDat
n16:2014
n3:domaciTvurceVysledku
n13:9161783 n13:7099053
n3:druhVysledku
n10:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n5:predkladatel
n3:idSjednocenehoVysledku
61785
n3:idVysledku
RIV/49777513:23510/13:43919711
n3:jazykVysledku
n7:eng
n3:klicovaSlova
Markov chain analysis, transition probability matrix, stock index, trend prediction, time series analysis.
n3:klicoveSlovo
n4:time%20series%20analysis. n4:trend%20prediction n4:transition%20probability%20matrix n4:stock%20index n4:Markov%20chain%20analysis
n3:kontrolniKodProRIV
[8EA880ED8193]
n3:mistoKonaniAkce
Jihlava
n3:mistoVydani
Jihlava
n3:nazevZdroje
Mathematical Methods in Economics 2013: 31st International Conference: Proceedings
n3:obor
n21:BB
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n16:2013
n3:tvurceVysledku
Svoboda, Milan Lukáš, Ladislav
n3:typAkce
n17:EUR
n3:zahajeniAkce
2013-09-11+02:00
s:numberOfPages
6
n20:hasPublisher
College of Polytechnics Jihlava
n14:isbn
978-80-87035-76-4
n6:organizacniJednotka
23510