Literature review on Artificial Neural Networks Techniques Application for Stock Market Prediction and as Decision Support Tools

Abstract

Objectives: This literature review is aiming to explore the use Artificial Neural Network (ANN) techniques in the field of stock market prediction. Design: Content analysis research technique. Data sources: Information retrieved from ProQuest electronic databases. Review methods: Utilizing key terms and phrases associated with Artificial Neural Network Stock Market Prediction from 2013–2018. Out of the 129 scholarly journal reviewed, there are 4 stock market studies met the inclusion criteria. The analysis and the evaluation includes 6 ANN derivatives techniques used to predict. Results: Findings from the reviewed studies revealed that all studies shows consistency that the accuracy rate of ANN stock market prediction is high. 2 Studies shows accuracy above 90%, 2 studies shows accuracy above 50%. Conclusion: This study reveals that the ability of ANN shows consistency of an accuracy rate of stock market prediction. Four method in predicting stock market had an accuracy above 95%. The highest accuracy achieved by using Signal Processing/Gaussian Zero-Phase Filter (GZ-Filter) with 98.7% prediction accuracy