Predicting Stock Market Price Movement Using Machine Learning Technique: Evidence from India
- Title
- Predicting Stock Market Price Movement Using Machine Learning Technique: Evidence from India
- Creator
- Praveen Kumar T.; Mallieswari R.; Kirupa Priyadarsini M.
- Description
- The stock market is uncertain, volatile, and multidimensional. Stock prices have been difficult to predict since they are influenced by a variety of factors. In order to make critical investment and financial decisions, investors and analysts are interested in predicting stock prices. Predicting a stock's price entails developing price pathways that a stock might take in the future. ANN and mathematical Geometric Brownian movement technique were employed in this study to forecast a stock market closing price of Indian companies. The comparative analysis indicates that the Geometric Brownian Method is better than ANN in giving better MAPE and RMSE Values. 2022 IEEE.
- Source
- 2022 International Conference on Interdisciplinary Research in Technology and Management, IRTM 2022 - Proceedings
- Date
- 2022-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Artificial Neural network; GBM; Investment component; Machine Learning; Stock Price Prediction
- Coverage
- Praveen Kumar T., Business And Management Christ, (Deemed To Be University), Bengaluru, India; Mallieswari R., Ramaiah Institute Of Management, Management Department, India; Kirupa Priyadarsini M., Psg Institute Of Management, Management Department, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-166547886-1
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Praveen Kumar T.; Mallieswari R.; Kirupa Priyadarsini M., “Predicting Stock Market Price Movement Using Machine Learning Technique: Evidence from India,” CHRIST (Deemed To Be University) Institutional Repository, accessed November 4, 2025, https://archives.christuniversity.in/items/show/20286.