Analysing Stock Market Trend Prediction using Machine & Deep Learning Models: A Comprehensive Review

Abstract

The applications of intelligent financial forecasting play an utmost important role in facilitating the investment decisions activities of many investors. With the right insight information, the investors can tailor their portfolio to maximise return while minimising risks. However, not every investment guarantees a good return, and this is mainly because most investors have limited information and skills to predict the stock trend. Nevertheless, the complex, chaotic and volatile nature of the stock market make any prediction attempts extremely difficult. This paper aims to provide a comprehensive review of the exiting researches which related to the application of Machine Learning and Deep Learning models in financial market forecasting domain. To prepare for this project, more than sixty research papers were analysed in-depth to extract required quantitative information, applications, and results on different methodologies. It is found from this project that Deep Learning outperformed Machine Learning in all the collected research papers, and it is the most suitable methodologies to apply to the stock market forecasting domain.