Artificial Intelligence-Based Approaches for Anticipating Financial Market Index Trends
- Title
- Artificial Intelligence-Based Approaches for Anticipating Financial Market Index Trends
- Creator
- Jose E.; Kappil S.R.; Cheriyan A.
- Description
- The stock market is an essential component of the world economy and significantly impacts how different countries handle their finances. Predicting stock prices has gained popularity recently since it can offer traders, investors, and policymakers useful information. Making informed financial decisions, lowering risk, and maximizing returns can all be facilitated by accurate stock price projections. Stock price prediction is a current research subject due to improvements in machine learning (ML) techniques, and several methodologies have been put forth in the literature. To increase the accuracy of stock price prediction, one method combines the feature extraction ability of convolutional neural networks (CNNs) with the classification strength of support vector machines (SVMs). CNNs are a subclass of neural networks that have excelled in voice and picture recognition. They can be taught to extract valuable features from the supplied data automatically. Contrarily, SVMs are a well-liked machine learning (ML) technique that has been applied for regression and classification tasks. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar.
- Source
- Artificial Intelligence in Forecasting: Tools and Techniques, pp. 173-189.
- Date
- 2024-01-01
- Publisher
- CRC Press
- Coverage
- Jose E., Department of Data Science, CHRIST University, India; Kappil S.R., Department of Data Science, CHRIST University, India; Cheriyan A., School of Business and Management, CHRIST University, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-104005150-4; 978-103250615-9
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Jose E.; Kappil S.R.; Cheriyan A., “Artificial Intelligence-Based Approaches for Anticipating Financial Market Index Trends,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 22, 2025, https://archives.christuniversity.in/items/show/18085.