IJBTT

Stock Market Prediction Using Novel Deep Learning Approaches: A Review
	© 2021 by IJCTT Journal
	Volume-69 Issue-7
	Year of Publication : 2021
	Authors : Munjal Shah
	DOI :  10.14445/22312803/IJCTT-V69I7P105

How to Cite?

Munjal Shah, "Stock Market Prediction Using Novel Deep Learning Approaches: A Review," International Journal of Computer Trends and Technology, vol. 69, no. 7, pp. 35-42, 2021. Crossref, https://doi.org/10.14445/22312803/IJCTT-V69I7P105

Abstract
Stock market prediction has been an intriguing subject for academic researchers and financial experts for a long time. Stock data is highly volatile, which makes stock price prediction a difficult challenge. Financial experts use a combination of various fundamental and technical stock analysis techniques to understand market trends and make decisions. Countless studies have presented numerous stock prediction frameworks. But the researchers are still on the quest of achieving better accuracy to maximize profits. Recent advancements in deep learning have enabled researchers to develop various stock prediction techniques which outperform previous methodologies. This review study is aimed at analyzing 15 novel deep learning-based stock prediction techniques from 2020-2021. These studies are selected from journals of notable publishers. The review includes a discussion of datasets, models, evaluation metrics, and results obtained by these techniques. Researchers have employed a combination of techniques ranging from word embedding algorithms, candlestick charts analysis, deep neural networks, deep reinforcement learning, Long Short Term Memory (LSTM), and Convolutional Neural Network (CNN) to perform stock market prediction. CNN and LSTM based predictive models have shown great results. All novel approaches achieved better results compared to previous state-of-the-art techniques. It can be concluded that stock market prediction is very complex due to its volatile and chaotic nature. This survey highlights the open issues of stock market prediction and aims to serve as a guideline for future research directions. As the technology progresses, we’ll continue to observe better deep learning approaches for stock market forecasting.

Keywords
Stock Prediction, Technical Market Analysis, Deep Learning, Reinforcement Learning, LSTM.

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