 Prediction of the Stock Prices at Uganda Securities Exchange Using the Exponential Ornstein–Uhlenbeck ModelJuma Kasozi, Erina Nanyonga, Fred Mayambala and Jewgeni Dshalalow International Journal of Mathematics and Mathematical Sciences, 2023, vol. 2023, 1-8 Abstract: We use the exponential Ornstein–Uhlenbeck model to predict the stock price dynamics over some finite time horizon of interest. The predictions are the key to the investors in a financial market because they provide vital reference information for decision making. We estimated all the parameters of the model (mean reversion speed, long-run mean, and the volatility) using the data from Stanbic Uganda Holdings Limited. We used the parameters to forecast the stock price and the associated mean absolute percentage error (MAPE). The predictions were compared against those by the ARMA-GARCH model. We also found the 95% prediction intervals before and during the COVID-19 pandemic. Results indicate that the exponential Ornstein–Uhlenbeck stochastic model gives very accurate and reliable predictions with a MAPE of 0.4941%. All the forecasted stock prices were within the prediction region established. This was not the case during the COVID-19 pandemic; the predicted stock prices are higher than the actual prices, indicating the severe impact COVID-19 inflicted on the stock market. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hin:jijmms:2377314 DOI: 10.1155/2023/2377314 Access Statistics for this article More articles in International Journal of Mathematics and Mathematical Sciences from Hindawi |
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