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Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions - 2021

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey On Methodologies, Recent Developments, And Future Directions

Research Area:  Machine Learning


With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.


Author(s) Name:  Nusrat Rouf,Majid Bashir Malik ,Tasleem Arif ,Sparsh Sharma,Saurabh Singh ,Satyabrata Aich and Hee-Cheol Kim

Journal name:  Electronics

Conferrence name:  

Publisher name:  MDPI

DOI:  10.3390/electronics10212717

Volume Information:  Volume 10 Issue 21