Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique
Adebola K. Ojo *
Department of Computer Science, University of Ibadan, Ibadan, Nigeria.
Ifechukwude Jude Okafor
Department of Computer Science, University of Ibadan, Ibadan, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Investors and stock market analysts face major challenges in predicting stock returns and making wise investment decisions. The predictability of equity stock returns can boost investor confidence, but it remains a difficult task. To address this issue, a study was conducted using a Long Short-term Memory (LSTM) model to predict future stock market movements. The study used a historical dataset from the Nigerian Stock Exchange (NSE), which was cleaned and normalized to design the LSTM model. The model was evaluated using performance metrics and compared with other deep learning models like Artificial and Convolutional Neural Networks (CNN). The experimental results showed that the LSTM model can predict future stock market prices and returns with over 90% accuracy when trained with a reliable dataset. The study concludes that LSTM models can be useful in predicting financial time-series-related problems if well-trained. Future studies should explore combining LSTM models with other deep learning techniques like CNN to create hybrid models that mitigate the risks associated with relying on a single model for future equity stock predictions.
Keywords: Long-short term memory, deep neural networks, forecasting, convolutional neural networks, performance evaluations