Abstract:
This study uses the Geometric Fractional Brownian Motion (GFBM) model to simulate
stock price path and test whether the simulated stock prices mimic the actual stock
returns. The model incorporates Hurst index which delimit the constant volatility
assumptions and was estimated using the moment generating function. The sample
for this study was based on the large Ghanaian companies listed on the Ghana Stock
Exchange (GSE). Daily stock price data was obtained from the GSE database over the
period January 2018 to December 2018. The results find increasing evidence that,
the GFBM model consistently predict the stock price over all time horizon. There was
a little above 80% chance that a stock price simulated using GFBM move in the same
direction as the actual stock price. Finally, the average percentage error of the GFBM
model was 16.68% or an accuracy of 83.32% while the GBM model generated an
average percentage error of 20.90% or an accuracy of 79.10%. This indicates that,
the GFBM model yielded better predicting accuracy than that of the GBM on almost
all the selected stocks in the long-run and partly in the short-run.
