Research Article
Deep Learning Analysis of Australian Stock Market Price Prediction for Intelligent Service Oriented Architecture
@INPROCEEDINGS{10.1007/978-3-030-95987-6_12, author={Muhammad Raheel Raza and Saleh Alkhamees}, title={Deep Learning Analysis of Australian Stock Market Price Prediction for Intelligent Service Oriented Architecture}, proceedings={IoT as a Service. 7th EAI International Conference, IoTaaS 2021, Sydney, Australia, December 13--14, 2021, Proceedings}, proceedings_a={IOTAAS}, year={2022}, month={7}, keywords={Stock market Price prediction LSTM GRU Service oriented architecture}, doi={10.1007/978-3-030-95987-6_12} }- Muhammad Raheel Raza
Saleh Alkhamees
Year: 2022
Deep Learning Analysis of Australian Stock Market Price Prediction for Intelligent Service Oriented Architecture
IOTAAS
Springer
DOI: 10.1007/978-3-030-95987-6_12
Abstract
Stock exchanges are economic entities facilitating various trading assets like monetary values, activities, valuable metals, etc., among stockbroker participants. Prediction of Stock market rates and observing the behaviour of daily closing rates is a crucial task for many businesses and investment authorities. This acts as a precaution to know the suitable period for stakeholders to invest. Deep Learning, in this regard, is considered to perform forecasting tasks efficiently with better accuracy. For this purpose, our study performs forecasting of Australian Stock Market daily closing rates based on Deep Learning approaches of LSTM and GRU from January 4 2000, to January 17 2017. This work predicts the closing rates for the next 216 days. A comparative analysis of prediction accuracy between Deep Learning methods like Long Short-Term Memory (LSTM) along with Gated Recurrent Unit (GRU) is performed. Results reveal that the deep learning model LSTM performs better than the other approach based on the results obtained. Performance of the models is measured using metrics such as RMSE and R2scores, where LSTM achieved a comparatively less RMSE value of 0.072 and the largest R2score of 0.855.
