A systematic literature survey on recent trends in stock market prediction

• Published in: PeerJ. Computer science Vol. 10; p. e1700
• Main Authors: Balasubramanian, Prakash, P., Chinthan, Badarudeen, Saleena, Sriraman, Harini
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 31.01.2024; PeerJ, Inc; PeerJ Inc
• Subjects: Artificial Intelligence; Data Mining and Machine Learning; Deep learning; Forecasts and trends; Global marketing; Literature reviews; Machine learning; Neural Networks; Research methodology; Sentiment analysis; Social networks; Stock market prediction; Stock markets; Stocks; Surveys; Trends; China; India; Deep learning; Artificial intelligence; Stock market prediction; Machine learning
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.1700

Prediction of the stock market is a challenging and time-consuming process. In recent times, various research analysts and organizations have used different tools and techniques to analyze and predict stock price movements. During the early days, investors mainly depend on technical indicators and fundamental parameters for short-term and long-term predictions, whereas nowadays many researchers started adopting artificial intelligence-based methodologies to predict stock price movements. In this article, an exhaustive literature study has been carried out to understand multiple techniques employed for prediction in the field of the financial market. As part of this study, more than hundreds of research articles focused on global indices and stock prices were collected and analyzed from multiple sources. Further, this study helps the researchers and investors to make a collective decision and choose the appropriate model for better profit and investment based on local and global market conditions.