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dc.contributor.authorJoseph, Samuel
dc.contributor.authorMduma, Neema
dc.contributor.authorNyambo, Devotha
dc.date.accessioned2023-04-06T09:33:01Z
dc.date.available2023-04-06T09:33:01Z
dc.date.issued2023-04-02
dc.identifier.urihttps://doi.org/10.48084/etasr.5710
dc.identifier.urihttps://dspace.nm-aist.ac.tz/handle/20.500.12479/1859
dc.descriptionThis research article was published by Engineering, Technology & Applied Science Research in Volume: 13 | Issue: 2 | Pages: 10517-10522 | April 2023 |en_US
dc.description.abstractStock price prediction models help traders to reduce investment risk and choose the most profitable stocks. Machine learning and deep learning techniques have been applied to develop various models. As there is a lack of literature on efforts to utilize such techniques to predict stock prices in Tanzania, this study attempted to fill this gap. This study selected active stocks from the Dar es Salaam Stock Exchange and developed LSTM and GRU deep learning models to predict the next-day closing prices. The results showed that LSTM had the highest prediction accuracy with an RMSE of 4.7524 and an MAE of 2.4377. This study also aimed to examine whether it is significant to account for the outstanding shares of each stock when developing a joint model for predicting the closing prices of multiple stocks. Experimental results with both models revealed that prediction accuracy improved significantly when the number of outstanding shares of each stock was taken into account. The LSTM model achieved an RMSE of 10.4734 when the outstanding shares were not taken into account and 4.7524 when they were taken into account, showing an improvement of 54.62%. However, GRU achieved an RMSE of 12.4583 when outstanding shares were not taken into account and 8.7162 when they were taken into account, showing an improvement of 30.04%. The best model was implemented in a web-based prototype to make it accessible to stockbrokers and investment advisors.en_US
dc.language.isoenen_US
dc.publisherEngineering, Technology & Applied Science Researchen_US
dc.subjectDeep learningen_US
dc.subjectStock price predictionen_US
dc.subjectDar es Salaam stock exchangeen_US
dc.titleA Deep Learning Model for Predicting Stock Prices in Tanzaniaen_US
dc.typeArticleen_US


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