title: A methodology for stochastic analysis of share prices as Markov chains with finite states.
abstract: Price volatilities make stock investments risky, leaving investors in critical position when uncertain decision is made. To improve investor evaluation confidence on exchange markets, while not using time series methodology, we specify equity price change as a stochastic process assumed to possess Markov dependency with respective state transition probabilities matrices following the identified state pace (i.e. decrease, stable or increase). We established that identified states communicate, and that the chains are aperiodic and ergodic thus possessing limiting distributions. We developed a methodology for determining expected mean return time for stock price increases and also establish criteria for improving investment decision based on highest transition probabilities, lowest mean return time and highest limiting distributions. We further developed an R algorithm for running the methodology introduced. The established methodology is applied to selected equities from Ghana Stock Exchange weekly trading data.
authors.affiliation: Department of Statistics, University of Ghana, Accra, Ghana.
authors.forename: Felix Okoe
authors.initials: FO
authors.lastname: Mettle
authors.affiliation: Department of Statistics, University of Ghana, Accra, Ghana.
authors.forename: Enoch Nii Boi
authors.initials: EN
authors.lastname: Quaye
authors.affiliation: Department of Banking and Finance, University of Professional Studies, Accra, Ghana.
authors.forename: Ravenhill Adjetey
authors.initials: RA
authors.lastname: Laryea
keywords: Expected mean return time; Limiting distribution; Markov chain; Markov process; Transition probability matrix