Stock Price Prediction of ReconAfrica (RECAF) Using Gated Recurrent Unit (GRU): Analysis and Implications for Investment Decisions
(1) Tomás López Aníbal Mail (University of Otago, New Zealand)
(2) * Rabiu Okanlawon Mail (University of Ghanaiversity of Pretoria, Ghana)
*corresponding author

Abstract


This study develops a stock price prediction model for ReconAfrica (RECAF) using Gated Recurrent Unit (GRU), an effective deep learning method for capturing temporal and non-linear patterns in stock price data. The model was trained and tested using five years of historical RECAF stock price data and evaluated with metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The evaluation results show that the GRU model achieved an MAE of 0.0992, MSE of 0.0397, RMSE of 0.1993, and MAPE of 4.27, indicating a high predictive capability. These findings underscore the potential of the GRU model as a valuable tool for investors and market analysts in making more informed investment decisions. While the results are promising, the study also identifies opportunities for further development through the integration of external data and exploration of other deep learning architectures. Thus, this research contributes significantly to stock market analysis and improved investment strategies.

   

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https://doi.org/10.33292/ijarlit.v2i2.35 		      

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