Predicting the Stock Markets Using Neural Network with Auxiliary Input
- Title
- Predicting the Stock Markets Using Neural Network with Auxiliary Input
- Creator
- Sakshi; Ghosh S.
- Description
- Predicting the stock market has always been a challenging task and has always had a certain appeal for researchers all around the world. Stock markets are supposed to be quite random and people with experience in the market strongly agree to the fact. Thus, predicting the stock market accurately paves the way for endless money. To date, no such algorithm has been devised that could even predict the stock market with a 90% accuracy rate. The difficulty lies in the randomness of the markets, and the various complexities involved in modeling market dynamics. Nevertheless, there have been algorithms with a decent success rate and researchers around the world have been in a constant attempt to improve over them. Thus, through this paper we attempt at predicting the return of a stock over a period of 10days after a particular news was out regarding the stock using the headlines of the news and certain other features important in determining the direction of a stock. The model was implemented with a sigma score of 0.81. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Lecture Notes in Networks and Systems, Vol-190, pp. 743-752.
- Date
- 2021-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Deep learning; Prediction; Stock market; Text classification
- Coverage
- Sakshi, Computer Science and Engineering, Siddaganga Institute of Technology, Tumkur, India; Ghosh S., Computer Science and Engineering, Christ University, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-981160881-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Sakshi; Ghosh S., “Predicting the Stock Markets Using Neural Network with Auxiliary Input,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/20626.