Volume 2, Issue 6, November 2017, Page: 116-125
Stochastic Modeling of Stock Price Behavior on Ghana Stock Exchange
Osei Antwi, Mathematics & Statistics Department, Accra Technical University, Accra, Ghana
Received: Sep. 12, 2017;       Accepted: Sep. 27, 2017;       Published: Nov. 14, 2017
DOI: 10.11648/j.ijssam.20170206.12      View  1888      Downloads  112
Abstract
This paper analyzes stock price behaviour on Ghana Stock Exchange (GSE) and develops a stochastic model to predict the behaviour of stock prices on the exchange using Monte Carlo simulations. The first part looks at the various justifications and models that have been put forward to explain stock behaviour and its distribution elsewhere. It traces the foundations of the use of stochastic process as a means of predicting stock price behaviour from Louis Bachelier normality assumption to the works of Samuelson’s lognormal supposition through to the doctoral thesis of Fama French in which he premised the behaviour of stock price to the idea of a random walk. We subsequently apply the Geometric Brownian Motion formulation to simulate stock price behaviour for all listed stocks on the GSE for the coming year (2015) using historical volatility and mean returns of the previous year (2014). The results find increasing evidence that the stochastic model consistently predict the stock price behaviour on the exchange in more than 80% of the listed stocks.
Keywords
Stock Price, Geometric Brownian Motion, Stock return, Stock Volatility, Monte Carlo Simulation
To cite this article
Osei Antwi, Stochastic Modeling of Stock Price Behavior on Ghana Stock Exchange, International Journal of Systems Science and Applied Mathematics. Vol. 2, No. 6, 2017, pp. 116-125. doi: 10.11648/j.ijssam.20170206.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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