A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market

Abstract

Recurrent Neural Networks (RNNs) is a sub type of neural networks that use feedback connections. Several types of RNN models are used in predicting financial time series. This study was conducted to develop models to predict daily stock prices of selected listed companies of Colombo Stock Exchange (CSE) based on Recurrent Neural Network (RNN) Approach and to measure the accuracy of the models developed and identify the shortcomings of the models if present. Feedforward, Simple Recurrent Neural Network (SRNN), Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) architectures were employed in building models. Closing, High and Low prices of past two days were selected as input variables for each company. Feedforward networks produce the highest and lowest forecasting errors. The forecasting accuracy of the best feedforward networks is approximately 99%. SRNN and LSTM networks generally produce lower errors compared with feedforward networks but in some occasions, the error is higher than feed forward networks. Compared to other two networks, GRU networks are having comparatively higher forecasting errors.