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Hurst Exponent Analysis on the Ghana Stock Exchange
American Journal of Mathematical and Computer Modelling
Volume 5, Issue 3, September 2020, Pages: 77-82
Received: May 29, 2020; Accepted: Jun. 11, 2020; Published: Aug. 25, 2020
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Isaac Ampofi, Mathematical Sciences Department, University of Mines and Technology, Tarkwa, Ghana
Akyene Tetteh, Management Studies Department, University of Mines and Technology, Tarkwa, Ghana
Eric Neebo Wiah, Mathematical Sciences Department, University of Mines and Technology, Tarkwa, Ghana
Sampson Takyi Appiah, Mathematics and Statistics Department, University of Energy and Natural Resources, Sunyani, Ghana
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This paper talks about the application of Hurst Index on the Ghana Stock Exchange (GSE). The aim of the paper was to find out, whether GSE daily returns have some autocorrelation (long-term dependency) and multifractality using the Hurst Index analysis. Hurst Index of daily returns of some selected stocks in the period of January 2018 to December 2018 constituting 247 trading days were computed using Rescale Range Method and the Periodogram Method. The findings show that, 91.7% of the stocks considered possess long-term dependency and only 8.3% shows multifractality. Besides, the average percentage error of the geometric fractional Brownian motion (GFBM) model was 16.68% with an efficiency accuracy of 83.32% whilst that of the geometric Brownian motion (GBM) model percentage error is 20.90% with an accuracy of 79.10%. This indicates that, the GFBM model yielded better predicting accuracy than GBM in the long-run and par predicting accuracy in the short-run.
Stock Price, Hurst Exponent, Geometric Brownian Motion, Geometric Fractional Brownian Motion, Ghana Stock Exchange, Drift, Volatility, Ghana Commercial Bank
To cite this article
Isaac Ampofi, Akyene Tetteh, Eric Neebo Wiah, Sampson Takyi Appiah, Hurst Exponent Analysis on the Ghana Stock Exchange, American Journal of Mathematical and Computer Modelling. Vol. 5, No. 3, 2020, pp. 77-82. doi: 10.11648/j.ajmcm.20200503.13
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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