Browsing by Author "Joseph, Samuel"

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Deep learning model for predicting stock prices in Tanzania
(NM-AIST, 2023-06) Joseph, Samuel
e prediction models help to provide investors with tools for making better data driven decisions. Machine learning and deep learning techniques have been successively utilized in various countries to develop these models. However, there is a shortage of literature on the efforts to exploit these techniques to forecast stock prices in Tanzania. Hence, this study was conducted to address this gap. The study selected active companiesfrom the Dar es Salaam Stock Exchange (DSE) and developed Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU) models to forecast the next day closing prices of the companies. Long Short-Term Memory was the overall best model with a Root Mean Square Error (RMSE) of 4.1818 and Mean Absolute Error (MAE) of 2.1695. Findings revealed that it was significant to account for the number of outstanding shares of each company when developing a joint model for forecasting the stock prices of multiple companies. Specifically, LSTM attained an RMSE of 10.4734 before accounting for outstanding shares and 4.7424 after accounting for outstanding shares, showing an improvement of 54.72%. Furthermore, findings showed that investors’ participation attributes helped to improve prediction accuracy. Specifically, LSTM realized an RMSE of 4.1818 when these attributes were appended from that of 4.7424 without them, showing an improvement of 11.8%. The resulting model was deployed in a web-based prototype, whereby, end-user validation results indicated that 76% of respondents rated the system as High in terms of its forecasting ability. In future, the study recommends exploration of more features.
A Deep Learning Model for Predicting Stock Prices in Tanzania
(Engineering, Technology & Applied Science Research, 2023-04-02) Joseph, Samuel; Mduma, Neema; Nyambo, Devotha
Stock 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.