| Autor: |
Prakash Balasubramanian, Chinthan P., Saleena Badarudeen, Harini Sriraman |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
PeerJ Computer Science, Vol 10, p e1700 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2376-5992 |
| DOI: |
10.7717/peerj-cs.1700 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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