A methodology for stochastic analysis of share prices as Markov chains with finite states
Date
2014-11
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
SpringerPlus
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. © 2014, Mettle et al.; licensee Springer.
Description
Keywords
Expected mean return time, Limiting distribution, Markov chain, Markov process, Transition probability matrix
Citation
Mettle, F.O., Quaye, E.N.B. & Laryea, R.A. SpringerPlus (2014) 3: 657. https://doi.org/10.1186/2193-1801-3-657